System and method for dynamic multi-objective optimization of machine selection, integration and utilization

ABSTRACT

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 12/242,525, entitled SYSTEM AND METHOD FOR DYNAMICMULTI-OBJECTIVE OPTIMIZATION OF MACHINE SELECTION, INTEGRATION ANDUTILIZATION, filed on Sep. 30, 2008, which is a continuation in partapplication of U.S. patent application Ser. No. 10/674,966 (now U.S.Pat. No. 7,797,062, issued Sep. 14, 2010), entitled SYSTEM AND METHODFOR DYNAMIC MULTI-OBJECTIVE OPTIMIZATION OF MACHINE SELECTION,INTEGRATION AND UTILIZATION, filed on Sep. 30, 2003, which is acontinuation in part application of U.S. patent application Ser. No.10/214,927 (now U.S. Pat. No. 6,847,854, issued Jan. 25, 2005), entitledSYSTEM AND METHOD FOR DYNAMIC MULTI-OBJECTIVE OPTIMIZATION OF MACHINESELECTION, INTEGRATION AND UTILIZATION, filed on Aug. 7, 2002, whichclaims the benefit of U.S. Provisional Patent Application Ser. No.60/311,880, filed Aug. 13, 2001, entitled INTELLIGENT PUMPING SYSTEMSENABLE NEW OPPORTUNITIES FOR ASSET MANAGEMENT AND ECONOMIC OPTIMIZATION,and U.S. Provisional Patent Application Ser. No. 60/311,596, filed Aug.10, 2001, entitled INTELLIGENT PUMPING SYSTEMS ENABLE NEW OPPORTUNITIESFOR ASSET MANAGEMENT AND ECONOMIC OPTIMIZATION, the entire contents ofwhich applications are hereby incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to the art of dynamic diagnostics andprognostics of systems, machines, processes and computing devices; andmore particularly the invention relates to control system and methodsfor selecting, controlling and optimizing utilization of machineryprimarily in an industrial automation environment. The inventionprovides for integration of control methods and strategies with decisionsupport and logistics systems to optimize specifically definedoperational and performance objectives.

BACKGROUND

The global economy has forced many businesses to operate and conductbusiness in an ever increasingly efficient manner due to increasedcompetition. Accordingly, inefficiencies that were once tolerated bycorporations, due to a prior parochial nature of customers andsuppliers, now have to be removed or mitigated so that the respectivecorporations can effectively compete in a vastly dynamic, globalmarketplace. Furthermore, the intense desire to operate “green”facilities that are environmentally friendly and to insure worker safetyprovides additional motivation to minimize waste, scrap, and insure areliable, safe process that will not fail unexpectedly.

Many industrial processes and machines are controlled and/or powered byelectric motors. Such processes and machines include pumps providingfluid transport for chemical and other processes, fans, conveyorsystems, compressors, gear boxes, motion control devices, HVAC systems,screw pumps, and mixers, as well as hydraulic and pneumatic machinesdriven by motors. Such motors are combined with other system components,such as valves, pumps, furnaces, heaters, chillers, conveyor rollers,fans, compressors, gearboxes, and the like, as well as with appropriatepower control devices such as motor starters and motor drives, to formindustrial machines and actuators. For example, an electric motor may becombined with a motor drive providing variable electrical power to themotor, as well as with a pump, whereby the motor rotates the pump shaftto create a controllable pumping system.

The components parts used to build such motorized systems (e.g., pumps,motors, motor drives . . . ) are commonly chosen according tospecifications for a particular application or process in which themotorized system is to be employed. For instance, a set ofspecifications for a motorized pumping system may include fluidproperties (e.g. viscosity, specific gravity), suction head available,flow rates or discharge pressures or ranges thereof, which the systemmust accommodate for use in a particular application. In such a case,the pump is chosen according to the maximum and minimum flow and headrequired in the application, and the motor is selected based on thechosen pump hydraulic power requirements, and other electrical andmechanical considerations. The corresponding motor drive is selectedaccording to the motor specifications. Other pumping system componentsmay then be selected according to the chosen motor, pump, motor drive,control requirements, and sensor input which may include motor speedsensors, pressure sensors, flow sensors, and the like.

Such system design specifications are typically driven by maximumoperating conditions, such as the maximum flow rate the pumping systemis to achieve, which in turn drives the specifications for the componentparts. For instance, the motor may be selected according to the abilityto provide the necessary shaft speed and torque for the pump to achievethe maximum required flow rate required for the process. Thus, thetypical motorized system comprises components rated according to maximumoperational performance needed. However, the system may seldom, if ever,be operated at these levels. For example, a pump system rated to achievea maximum flow rate of 100 gallons per minute (GPM) may be operated at amuch lower flow rate for the majority of its operating life.

In facilities where such motorized systems are employed, otheroperational performances characteristics may be of interest, apart fromthe rated output of the motorized system. For instance, the cost ofoperating a pumping system is commonly of interest in a manufacturingfacility employing the system. The component parts of such a pumpingsystem typically include performance ratings or curves relating to theefficiency of the component parts at various operating conditions. Theenergy efficiency, for example, may be a measure of the transferredpower of the component device, which may be expressed as a percentage ofthe ratio of output power (e.g., power delivered by the device) to inputpower (e.g., power consumed by the device). These performance curvestypically include one or more operating points at which the componentoperates at maximum efficiency. In addition to the optimal efficiencyoperating point, the components may have other operating points at whichother performance characteristics are optimal, such as expectedlifetime, mean time between failures (MTBF), acoustic emissions orvibration output, time between expected servicing, safety, pollutionemissions, or the like.

While the operating specifications for the components in a motorized(e.g., pumping) system may provide for component device selection toachieve one or more system operational maxima (e.g., maximum flow ratefor a pumping system), other performance metrics (e.g., efficiency,cost, lifetime, MTBF . . . ) for the components and/or the system ofwhich they form a part, are not typically optimal at the actualoperating conditions. Thus, even where the efficiency ratings for apump, motor, and motor drive in a motorized pumping system provide formaximum efficiency at or near the maximum flow rate specified for thepumping system, the efficiency of one or more of these components (e.g.,as well as that of the pumping system overall) may be relatively poorfor other flow rates at which the system may operate for the majority ofthe service life thereof. In addition, motors, pumps, and drives aresized to meet the application requirements. Each of these componentshave different operating characteristics such that the efficientoperating point of a motor is at a different speed and load than theefficient operating point of the connected pump. Separate selection ofcomponents based on cost or individual efficiencies will result in anintegrated system that is sub-optimal with regard to efficiency,throughput, or other optimization criteria.

Moreover, typically, the specification for such machines or componentsthereof is performed at an isolated or level of granularity such thathigher-level aspects of a business or industrial concern are overlooked.Thus, there is a need for methods and systems by which efficiency andother performance characteristics associated with selecting andutilizing motorized systems and components thereof may be improved.

SUMMARY

The following presents a simplified summary of the invention in order toprovide a basic understanding of one or more aspects of the invention.This summary is not an extensive overview of the invention. It isintended to neither identify key or critical elements of the invention,nor to delineate the scope of the present invention. Rather, the solepurpose of this summary is to present some concepts of the invention ina simplified form as a prelude to the more detailed description that ispresented hereinafter.

The subject invention provides for employing machine diagnostic and/orprognostic information in connection with optimizing an overall businessoperation. The scope of business operation can include plant-wide orenterprise business objectives and mission objectives such as forexample that which may be required for aircraft, Naval ships, nuclear,or military systems or components.

Systems, networks, processes, machines, computers . . . employing thesubject invention can be made to operate with improved efficiency, lessdown-time, and/or extended life, and/or greater reliability, as wellenhancing systems/processes that are a superset thereof. Diagnosticsand/or prognostics in accordance with the invention can be effecteddynamically as well as in situ with respect to variousoperations/processes. Moreover, the invention provides for optimizingutilization of diagnostic/prognostic schemes via employment of autility-based approach that factors cost associated with taking anaction (including an incorrect action or no action) with benefitsassociated with the action (or of inaction). Moreover, for example, suchaction can relate to dissemination of the diagnostic/prognostic dataand/or an action taken in connection with an analysis of the data. Thedata dissemination can be effected via polling techniques, beaconingtechniques, heartbeat schemes, broadcast schemes, watchdog schemes,blackboard schemes, and/or a combination thereof. Accordingly, stateinformation can be employed in order to determine which scheme orcombination or order would lead to greatest utility in connection withdesired goal(s).

The subject invention provides for addressing concerns associated withtaking automated action in connection with valuable and/or criticalsystems or methods. For example, security issues arise with respect topermitting automated action—the subject invention provides foremployment of various security based schemes (e.g., authentication,encryption, . . . ) to facilitate maintaining control as well as accessto such systems/processes. The invention can also take intoconsideration levels of security and criticality of processes/systems ofa network. For example, automated action in connection with a criticalprocess (e.g., power, life support, fire suppression, HVAC . . . ) canonly be taken after high security measures have been applied as well assuch action only being taken with a high-level confidence level (e.g.,99% probability of a correct inference) that the automated action is thecorrect action to take given the current evidence (e.g., current stateinformation and predicted state).

Moreover, another aspect of the invention provides for employment ofprognostics/diagnostics to optimize quality control of products to bemanufactured and/or delivered. For example, inference as to future stateof a component and effect of such future state on production of aproduct can be employed as part of a closed-loop system that providesfor adjusting processing parameters in situ so as to dynamically correctfor variances associated with the inferred state that could impactquality and/or quantity of the product. It is to be appreciated thatsuch techniques can be applied as part of an enterprise resourceplanning (ERP) system to facilitate forecasting events/parameters (e.g.,capacity, supplier throughput, inventory, production, logistics,billing, design, . . . ) that might impact an enterprise. As will bediscussed in greater detail infra, one particular aspect of theinvention can employ technologies such as radio frequency identification(RFID) tags in connection with failure prediction, product throughputanalysis, line diagnosis, inventory management, and production controlamong other technologies.

One particular aspect of the invention provides control systems andmethodologies for controlling a process having one or more motorizedpumps and associated motor drives, which provide for optimized processperformance according to one or more performance criteria, such asefficiency, component life expectancy, safety, electromagneticemissions, noise, vibration, operational cost, or the like. For example,such machine data can be employed in connection with inventory control,production, marketing, utilities, profitability, accounting, and otherbusiness concerns. Thus, the present invention abstracts such machinedata so that it can be employed in connection with optimizing overallbusiness operations as compared to many conventional systems that employmachine data solely in connection with machine maintenance, control andpossibly process control or optimal control methods.

Aspects of the subject invention employ various high-level dataanalysis, modeling and utilization schemes in connection with providingsome of the advantages associated with the invention. For example,Bayesian Belief Networks can be employed in connection with the subjectinvention. A probabilistic determination model and analysis can beperformed at various levels of data to factor the probabilistic effectof an event on various business concerns given various levels ofuncertainty as well as the costs associated with an making an incorrectinference as to prognosing an event and its associated weight withrespect to the overall business concern. Statistical, probabilistic, andevidence or belief-based, and/or various rule-based approaches can alsobe employed in connection with the invention. The present inventiontakes into consideration that the benefits of machinery monitoring andcondition-based maintenance can be significantly enhanced by integratingreal-time diagnostics and prognostics techniques within the framework ofan automatic control system. System operation can be prescribed based onthe predicted or probabilistic state or condition of the machinery inconjunction with the anticipated workload or demand or probabilisticdemand and the business strategy along with other operational andperformance constraints. The generated decision space may be evaluatedto facilitate that suitably robust operational and/or machinerydecisions are made that maximize specified business objective(s) such asrevenue generation, life cycle cost, energy utilization, and/ormachinery longevity. Thus the subject invention integrates diagnosticsand/or prognostics with control linked with business objectives andstrategies to provide unique opportunities for dynamic compensatingcontrol and ultimately for managing and optimizing system assetutilization. This may be performed in consideration for uncertainty andbelief in diagnostics and prognostics, control and performanceexpectations, and business uncertainties and likelihoods.

In accordance with another aspect of the invention, an intelligent agentscheme can be employed wherein various machines, physical entities,software entities, can be modeled and represented by intelligentsoftware agents that serve as proxies for the respective machines orentities. These agents can be designed to interact with one another andfacilitate converging on various modifications and control of themachines of entities in connection with efficiently optimizing anoverall business concern. Lower level agents can collaborate andnegotiate to achieve lower level process objectives in an optimal mannerand integrate this information to higher level agents. Agents, cancompete with each other for limited resources and become antagonistic inorder to realize critical objectives in a save, reliable, and optimummanner. Moreover, the agents can comprise a highly distributed systemcontrolling the operation of a complex dynamic process. There may notexist a central point or control or coordination of the system. Ratherinformation is distributed among the various agents. Groups of agentscan form clusters to promote meeting operational objectives such aslocal agent goals as well as to promote collaboration in meetinghigher-level system goals and objectives. During negotiation forservices and functions, local agents can also provide “cost” informationto other agents indicating efficiency, energy utilization, or robustnessfor example. Agents can assign functions and control modes to particularagents based on a comparison and optimization of the specified costfunction or operational objective or objectives to be optimized.

Moreover, it is to be appreciated the subject invention can be employedin connection with initial specification, layout and design of anindustrial automation system (e.g., process, factory) such thathigh-level business objectives (e.g., expected revenue, overhead,throughput, growth) are considered in connection with predicted machinecharacteristics (e.g., life cycle cost, maintenance, downtime, health,efficiency, operating costs) so as to converge on specifications,layout, and design of the industrial automation system so that a mappingto the high-level business objectives is more closely met as compared toconventional schemes where such layout and design is performed in moreor less an ad hoc, manual and arbitrary manner. Integrating informationregarding opportunities for real-time prognostics and optimizing controlcan influence the initial design and configuration of the system toprovide additional degrees of freedom and enhance the capability forsubsequent prognostics and optimizing and compensating control.

Predicted operating state(s) of the machine may be determined based onexpected demand or workload or a probabilistic estimate of futureworkload or demand. Similarly, expected environment (e.g., temperature,pressure, vibration, . . . ) information and possible expected damageinformation may be considered in establishing the predicted future stateof the system. Undesirable future states of the system can be avoided ordeferred through a suitable change in the control while achievingrequired operating objectives and optimizing established operational andbusiness objectives.

Discussing at least one aspect of the invention at a more granularlevel, solely for sake of understanding one particular context of theinvention, control systems and methods are provided for controlling amotorized system according to a setpoint (e.g., flow rate for amotorized pump system), operating limits, and a diagnostic signal,wherein the diagnostic signal is related to a diagnosed operatingcondition in the system (e.g., efficiency, motor fault, system componentdegradation, pump fault, power problem, pump cavitation . . . ). Theinvention thus provides for controlled operation of motors and motorizedsystems, wherein operation thereof takes into account desired processperformance, such as control according to a process setpoint, as well asone or more other performance characteristics or metrics, related to themotorized system and/or component devices therein, whereby improvementsin efficiency and other performance characteristics may be realized withallowable process and machinery operating constraints via considerationof prognostic and optimization data.

According to one aspect of the present invention, a method is providedfor controlling a motorized system. A desired operating point isselected within an allowable range of operation about a system setpointaccording to performance characteristics associated with a plurality ofcomponents in the system. For example, a flow rate setpoint may beprovided for a motorized pump system, and a range may be provided (e.g.,+/−10%) for the system to operate around the setpoint flow value. Thisrange may correspond to a permissible range of operation where theprocess equipment is making a good product. The system may be operatedat an operating point within this range at which one or more performancecharacteristics are optimized in accordance with the invention. Thus,for example, where an allowable flow control range and setpoint providefor control between upper and lower acceptable flow rates, the inventionprovides for selecting the operating point therebetween in order tooptimize one or more system and/or component performancecharacteristics, such as life cycle cost, efficiency, life expectancy,safety, emissions, operational cost, MTBF, noise, and vibration.

Where the motorized system includes an electric motor operativelycoupled with a pump and a motor drive providing electrical power to themotor, the performance characteristics may include efficiencies or othermetrics related to the motor, the pump, and/or the motor drive. Theselection of the desired operating point may comprise correlating one ormore of motor efficiency information, pump efficiency information, andmotor drive efficiency information in order to derive correlated systemefficiency information. The desired operating point can then be selectedas the optimum efficiency point within the allowable range of operationaccording to the correlated system efficiency information. Theefficiency of the individual component devices, and hence of the pumpingsystem, may be associated with the cost of electrical energy or powerprovided to the system. Consequently, the invention can be employed tocontrol the pumping system so as to minimize power consumed by thesystem, within tolerance(s) of the allowable range about the processsetpoint.

The invention thus allows a system operator to minimize or otherwiseoptimize the cost associated with pumping fluid, where for example, thecost per unit fluid pumped is minimized. Alternatively or incombination, other performance characteristics may be optimized oraccounted for in the optimization in order to select the desiredoperating point within the allowable range. For instance, the componentperformance information may comprise component life cycle costinformation, component efficiency information, component life expectancyinformation, safety information, emissions information, operational costinformation, component MTBF information, MTTR, expected repair cost,noise information, and/or vibration information. In this regard, it willbe recognized that the value of one or more system performance variables(e.g., temperature, flow, pressure, power . . . ) may be used indetermining or selecting the desired operating point, which may beobtained through one or more sensors associated with the system, a modelof the system, or a combination of these.

Another particular aspect of the invention provides a control system forcontrolling a process having a pump with an associated motor. Thecontrol system comprises a motor drive providing electrical power to themotor in a controlled fashion according to a control signal, and acontroller providing the control signal to the motor drive according toa desired operating point within an allowable range of operation about aprocess setpoint. The controller selects the desired operating pointaccording to performance characteristics associated one or morecomponents in the process. The system can further comprise a userinterface for obtaining from a user, the setpoint, allowable operatingrange, component performance information, and/or performancecharacteristic(s), which are to be optimized.

In addition, the system can obtain such information from a host computerand/or other information systems, scheduling systems, inventory systems,order entry systems, decision support systems, maintenance schedulingsystems, accounting systems or control systems among others within alarger process via a network or wireless communications. Moreover, thisinformation can be obtained via a wide area network or globalcommunications network, such as the Internet. In this regard, theoptimization of one or more performance characteristics can be optimizedon a global, enterprise-wide or process-wide basis, where, for example,a single pump system may be operated at a less than optimal efficiencyin order to facilitate the operation of a larger (e.g., multi-pump)process or system more efficiently. A specific pump may provide lowthroughput and run inefficiently to meet minimum product requirementsdue to the fact that another system in the enterprise can provideadditional processing at a much more cost-effective rate and will be runat maximum throughput.

Yet another aspect of the invention provides for operating a motorizedsystem, wherein a controller operatively associated with the systemincludes a diagnostic component to diagnose an operating conditionassociated with the pump. The operating conditions detected by thediagnostic component may include motor or pump faults, or failure and/ordegradation, and/or failure prediction (e.g., prognostics) in one ormore system components. The controller provides a control signal to thesystem motor drive according to a setpoint and a diagnostic signal fromthe diagnostic component according to the diagnosed operating conditionin the pump. The diagnostic component may perform signature analysis ofsignals from one or more sensors associated with the pump or motorizedsystem, in order to diagnose the operating condition.

Thus, for example, signal processing may be performed in order toascertain wear, failure, remaining useful lifetime, or other deleteriouseffects on system performance, whereby the control of the system may bemodified in order to prevent further degradation, extend the remainingservice life of one or more system components, or to prevent unnecessarystress to other system components. In this regard, the diagnosticcomponent may process signals related to flow, pressure, current, noise,vibration, and temperature associated with the motorized system. Thealtered system control may extend the life of the machinery to maximizethroughput while insuring there is not failure for a specified period oftime and not longer. Having the machinery live longer than the minimumnecessary will require operating the machinery at an even lower level ofefficiency. For example our objective may be to maximize throughput orefficiency while just meeting the minimum required lifetime and notlonger.

The aforementioned novel features of the subject invention can beemployed so as to optimize an overall business commensurate with setbusiness objectives. Moreover, as business needs/objectives change, theinvention can provide for dynamic adjustment and/or modification ofsub-systems (e.g., machines, business components, configurations,process steps, . . . ) in order to converge toward the new operatingmode that achieves the business objective in an optimum manner. Thus,the subject invention extracts and abstracts machine data (e.g.,diagnostic and/or prognostic data) and employs such data not only inconnection with optimizing machine utilization at a low level, but alsoto maximize utilization of a machine given constraints associated withhigh-level business objectives. Various models including simulationmodels, rule-based system, expert system, or other modeling techniquesmay be used to establish the range of possible operating conditions andevaluate their potential for optimizing machinery operation.

It is to be appreciated that in addition to industrial applications, thesubject invention can be employed in connection with commercial (e.g.HVAC) and military systems (e.g. Navy ships); and such employment isintended to fall within the scope of the hereto appended claims.

To the accomplishment of the foregoing and related ends, the invention,then, comprises the features hereinafter fully described. The followingdescription and the annexed drawings set forth in detail certainillustrative aspects of the invention. However, these aspects areindicative of but a few of the various ways in which the principles ofthe invention may be employed. Other aspects, advantages and novelfeatures of the invention will become apparent from the followingdetailed description of the invention when considered in conjunctionwith the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1a and 1b are schematic illustrations of prognostics systems inaccordance with various aspects of the subject invention.

FIG. 1c is a flow diagram illustrating state management in accordancewith a an aspect of the subject invention.

FIGS. 1d-1h illustrate system optimization aspects of the subjectinvention.

FIG. 1i illustrates a scheme that facilitates achieving a pre-planned,optimal future state in accordance with an aspect of the subjectinvention.

FIG. 1 j, illustrates another aspect of the subject invention relatingto establishing potential future state of a system/process.

FIG. 1k illustrates an enterprise resource planning system in accordancewith an aspect of the subject invention.

FIG. 2 illustrates exemplary operating levels of a pump system over timein accordance with the subject invention.

FIG. 3 graphically illustrates a gradient search technique in accordancewith the subject invention.

FIG. 4 illustrates an exemplary intelligent agent-based framework inaccordance with the subject invention.

FIG. 5 illustrates an exemplary belief network in accordance with thesubject invention.

FIG. 6 is a high level illustration of a distributed system inaccordance with the subject invention.

FIG. 7 illustrates a plurality of machines employing the subjectinvention in connection with optimization.

FIG. 8 is a high-level flow diagram in accordance with one particularaspect of the subject invention.

FIG. 9 is a side elevation view illustrating an exemplary motorized pumpsystem and a control system therefore with an optimization component inaccordance with an aspect of the present invention;

FIG. 10 is a schematic diagram illustrating further details of theexemplary control system of FIG. 9;

FIG. 11 is a schematic diagram further illustrating the efficiencyoptimization component and controller of FIGS. 9 and 10;

FIG. 12 is a plot showing an exemplary pump efficiency curve;

FIG. 13 is a plot showing an exemplary motor efficiency curve;

FIG. 14 is a plot showing an exemplary motor drive efficiency curve;

FIG. 15 is a plot showing an exemplary correlated pump system efficiencyoptimization curve in accordance with the invention;

FIG. 16 is a schematic diagram illustrating an exemplary fluid transfersystem having multiple pump and valve controllers networked forpeer-to-peer communication according to an aspect of the invention;

FIG. 17 is a schematic diagram illustrating another exemplary fluidtransfer system having a host computer as well as multiple pump andvalve controllers networked for peer-to-peer and/or host-to-peercommunication according to an aspect of the invention;

FIG. 18 is a schematic diagram illustrating an exemplary manufacturingsystem having multiple pump and valve controllers in which one or moreaspects of the invention may be implemented;

FIG. 19 is a flow diagram illustrating an exemplary method ofcontrolling a motorized pump in accordance with another aspect of theinvention; and

FIG. 20 is a side elevation view illustrating another exemplarymotorized pump system and a control system therefore with a diagnosticcomponent in accordance with another aspect of the invention.

FIG. 21 provides further illustration of an enterprise resource planningcomponent in accordance with an aspect of the claimed subject matter.

FIG. 22 provides yet further illustration of an enterprise resourceplanning component in accordance with various aspects of the claimedsubject matter.

FIG. 23 depicts a method that can be utilized to provide an energyoptimization model in accordance with an aspect of the claimed subjectmatter.

FIG. 24 depicts a method that can be utilized to provide dynamiccapacity management in accordance with an aspect of the claimed subjectmatter

FIGS. 25-31 illustrates various and disparate user manipulable visualinstrumentations that can be rendered by the claimed subject matter

DETAILED DESCRIPTION

The present invention is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe present invention may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the present invention.

As used in this application, the terms “component” and “system” areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution. For example, a component may be, but is not limited to being,a process running on a processor, a processor, an object, an executable,a thread of execution, a program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components may reside within a processand/or thread of execution and a component may be localized on onecomputer and/or distributed between two or more computers.

As used herein, the term “inference” refers generally to the process ofreasoning about or inferring states of the system, environment, and/oruser from a set of observations as captured via events and/or data.Inference can be employed to identify a specific context or action, asystem or component state or condition, or can generate a probabilitydistribution over states, for example. The inference can beprobabilistic—that is, the computation of a probability distributionover states of interest based on a consideration of data and events andthe combination of individual probabilities or certainties. For example,the probability of an observation can be combined with the probabilityassociated with the validity of the applicable inference rule or rules.Inference can also refer to techniques employed for composinghigher-level events or conditions from a set of more basic level events,conditions, observations, and/or data. Such inference results in theconstruction of new events, conditions, or actions from a set ofobserved events and/or stored event data, whether or not the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Any of a variety ofsuitable techniques for performing inference in connection withdiagnostics/prognostics in accordance with the subject invention can beemployed, and such techniques are intended to fall within the scope ofthe hereto appended claims. For example, implicitly and/or explicitlyclassifiers can be utilized in connection with performing aprobabilistic or statistical based analysis/diagnosis/prognosis—Bayesiannetworks, fuzzy logic, data fusion engines, hidden Markov Models,decision trees, model-based methods, belief systems (e.g.,Dempster-Shafer), suitable non-linear training schemes, neural networks,expert systems, etc. can be utilized in accordance with the subjectinvention.

The subject invention provides for system(s) and method(s) relating toemploying machine data in connection with optimizing an overall systemor process. The machine data can be collected dynamically (e.g., in theform of diagnostic data or control data) and/or generated in the form ofprognostic data relating to future machine state(s). The machine datacan be collected and/or generated in real-time (e.g., in situ,dynamically, without significant lag time from origination tocollection/generation). The machine data can be analyzed and theanalysis thereof employed in connection with optimizing machineutilization as well as other business components or systems (e.g.,accounting, inventory, marketing, human resources, scheduling,purchasing, maintenance manufacturing . . . ) so as to facilitateoptimizing an overall business objective or series of objectives orconcerns.

The invention provides methods and systems for controlling a motorizedsystem in order to achieve setpoint operation, as well as to optimizeone or more performance characteristics associated with the system whileoperating within specified operating constraints. The invention ishereinafter illustrated with respect to one or more motorized pumpsystems and controls thereof. However, it will be appreciated that oneor more aspects of the invention may be employed in operating othermotorized systems, including but not limited to fans, conveyor systems,HVAC systems, compressors, gear boxes, motion control devices, screwpumps, mixers, as well as hydraulic and pneumatic machines driven bymotors. Further other non-motorized systems are included in the scope ofthis invention including but not limited to ovens, transportationsystems, magnetic actuators, reaction vessels, pressurized systems,chemical processes, and other continuous processes. For example, thesubject invention can be employed to facilitate prognosing wear of metaland/or semiconductor contacts, switches, plugs, insulation, windings,bushings, valves, seals, . . . so that they can be replaced or repairedprior to failure. Thus, scheduling of thermographic inspections forexample can be conducted when actually required rather than on a fixedschedule. The invention can also be applied to corrosion prognostics aswell as latency and/or node failure or backlog predictions for networktraffic. The invention can be applied over a time horizon wherein timeis factored into a utility-based diagnosis and/or prognosis inconnection with the subject invention. For example, value ofinformation, states, actions, inactions can vary as a function of timeand such value densities can be considered in connection withdiagnostics and/or prognostics in connection with the subject invention.

Moreover, the subject invention can be applied to commercial systemssuch as fleet vehicles, commercial HVAC systems, elevators . . . as wellas aircrafts (commercial and military), ships (e.g., Navy warships),enterprise systems, resource planning systems, mission performance andstrategy planning, and a wide variety of other applications hereinprognoses can facilitate improvement of efficiency and/or optimization.

In addition, the attached figures and corresponding description belowillustrate the invention in association with optimizing system and/orcomponent efficiency, although it will be recognized that otherperformance characteristics of a motorized system may be optimizedindividually or in combination, which performance characteristics mayinclude, but are not limited to, life cycle cost, efficiency, lifeexpectancy, safety, throughput, emissions, operational cost, MTBF,noise, vibration, energy usage, and the like. Furthermore, the aspectsof the invention may be employed to provide for optimization at a highersystem level, wherein a process comprises a plurality of motorizedsystems as part of an overall automation system such that one or moreperformance characteristics of the entire process are optimizedglobally. Moreover, as discussed herein aspects of the invention can beemployed in connection with optimizing many higher level systems (e.g.,business-based system).

The higher-level system optimization may prescribe not operating at anoptimum efficiency point with regard to energy utilization. Rather, amore important, over-arching objective such as maximizing revenuegeneration can supercede more narrow, limited scope objectives ofachieving lowest energy usage or extending machinery lifetime. Thesubject invention employs a performance driven approach to leverage offdevelopments in diagnostic and prognostic algorithms, smart machines andcomponents, new sensor technologies, smart sensors, and integrate thesetechnologies among others in a framework of an enterprise-wide assetmanagement (EAM) system. The combination of optimizing methods andprocesses in the framework of an EAM system comprise an AssetOptimization System.

In addition to maintenance and repair costs, consideration for issuessuch as operational impact, business strategy, and supply chain (e.g.,connected supplier-manufacturer-customer) issues are also considered.There are several compelling business drivers that often makecost-effective machinery reliability not only economically sound, butalso a business imperative. These recent business drivers includegreater concern for protecting the environment, ultimate concern forworker safety, connected (e.g. virtual) organizations, make-to-orderoperating strategy, pay-for-performance (e.g., power-by-the-hour),containing warranty costs, and competitive time-based performance withgreater scrutiny and expectations in a rapidly expanding e-businessworld.

Although, the subject invention is primarily described in connectionwith motors and pumps, it is emphasized that the subject inventionapplies directly to other commercial and industrial processmachinery/systems. These systems could include for example a plant HVACsystem, a conveyor system, a semi-conductor fab line, chemicalprocessing (e.g. etching processes) or other continuous process ornon-motor driven machinery. Providing overall asset optimization asproposed herein can require integrating and optimizing other non-motorcomponents in a plant. The scope of the subject invention as defined bythe hereto appended claims is intended to include all such embodimentsand applications.

FIG. 1a illustrates a prognostics system 100 in accordance with oneparticular aspect of the invention. A prognostics engine 110 is coupledto a network 112—the coupling can be effected via hard-wire, wireless,Internet, optics, etc. The prognostics engine receives data relating tomachines 114 or processes that are part of the network. The data isdynamically analyzed within a desired context or set of rules forexample, and the engine 110 predicts/infers future state(s)/event(s)relating to the devices, clusters thereof, tertiary devices (or clustersthereof), processes, and/or the entire network. The prognostic engine110 can employ extrinsic context data as represented via block 116—it isto be appreciated that such context data (or a subset thereof) can beprovided by the machines as well as such context data being a priorisaved within the engine and/or a data store operatively coupled thereto.The context data 116 for example can relate to future load, futureenvironment, possible mission scenario, expected stress, etc.

The prognostics can be done in the context of an expected futureenvironment, stress level, or mission. Several prognostic results can begenerated based on possible or probable future environment or stressconditions. The prognostic data provided by the engine 112 can beemployed to take corrective action to mitigate undesirable effectsassociated with the predicted state. The prognostic data can also beemployed to take automated action in order to optimize the network or asubset thereof. Moreover, such data can be employed for forecasting,trending, scheduling, etc. As shown, the machines 114 (or a subsetthereof) can also comprise diagnostic/prognostic components 118 that canwork with the prognostics engine 110 in connection with diagnosingand/or prognosing the network and/or a subset thereof.

It is to be appreciated that the system can include a plurality ofprognostics engines 120 as shown in FIG. 1 b. The engines can servedifferent roles with respect to predicting various future states.Moreover, the engines can be part of a hierarchical organization whereinthe hierarchy can include various levels of control and function suchthat one engine may be an agent of another engine. Such arrangement canprovide for increasing speed of prognosis as well as isolating subsetsof the system for any of a variety of reasons (e.g., security, processcontrol, speed, efficiency, data throughput, load shedding . . . ).

As depicted in FIG. 1 a, the invention can take the form of adistributed prognostic system such that individual components canrespectively include prognostic engines that receive and analyze stateinformation with respect to the individual device(s). Accordingly, thedevices can communicate with one another and prognostic informationregarding device(s) can be shared as part of a collaborative effort toimprove accuracy of the aggregate system prognostics. It can also beutilized to improve operations of an overall system. It is to beappreciated that not all components of a network need to be intelligent(e.g., comprise prognostics components), and that certain devices canserve as an intelligent node with respect to other less intelligentdevices wherein the node and the other devices form a cluster. Therespective intelligent device can receive, monitor, and make predictionsas to future state of the cluster or subset thereof. It is to beappreciated that the intelligent nodes need not be fixed to a particularset of non-intelligent components, and that as part of a distributedintelligent system, clusters can be dynamically generated based oncurrent state of a larger group of components and state of the system aswell as current and/or future needs/concerns.

Similarly, a group of intelligent system components can dynamicallyre-configure based on the current system state or a predicted orpossible future system state. For example, a dynamic re-configurationmay enable the intelligent system components to more quickly or reliabledetect and respond to a system disturbance or fault that may possibleoccur in the future. Accordingly, for example, in a critical eventsituation, intelligent nodes can collaborate, negotiate use ofresources, alter function and control of intelligent components andshare resources (e.g., processing resources, memory resources,transmission resources, cooling capability, electrical power, . . . ) inorder to collectively detect, isolate, mitigate impact, circumvent andmaintain critical services, and restore functionality in an optimalmanner. Of course, a utility-based analysis can be employed inaccordance with the invention wherein cost of taking certain actionsgiven evidence can be applied against benefit of such action. Similarly,the cost—benefit of taking no action may be analyzed. In addition, theprobability of certain events, failures, environments, and cost impactmay be evaluated in the context of uncertainty or probability. Theresultant potential benefit from various prescribed actions isestablished in a probabilistic content or as a probability density valuefunction. The resulting analysis and action planning provides a basisfor prescribing an operational plan and series of decisions that willmaximize system performance, business benefit, or mission success withthe highest probability.

In accordance with an alternative aspect of the invention, intelligentcomponents can broadcast state/event change information about themselvesor a cluster related thereto in a heartbeat type manner so thatinformation is disseminated upon change of state. Such beacon-typescheme can facilitate optimizing network processing and transmissionbandwidth as compared to a polling scheme, for example. Moreover, aspart of an intelligent system, the broadcasting of data can be effectedsuch that devices that are or might be effected by such change of stateare notified while other devices do not receive such broadcast. Thebroadcast can be daisy-chained wherein one change of state can effectstate of other devices, which change of state effects even otherdevices, and thus the change of state info. can be part of a domino typedata dissemination scheme. It is to be appreciated that polling may alsobe desired in certain situations and the invention contemplates pollingin addition to broadcast.

FIG. 1c illustrates a high level methodology 130 relating to conveyanceof state information. At 132 state data (e.g., change of stateinformation) is received. The state data can be received by a componentof the device wherein the change took place, or a node of a cluster mayreceive data relating to change of state about and/or within the cluster. . . . At 134 it is determined if such change of state is potentiallyrelevant to the device, cluster, network, tertiary devices, processes,applications, individuals, entities, etc. If the data is relevant, at136 the data is forwarded to where the state change data might berelevant. If the data is not relevant, the process returns to 132 herechange of state is further monitored. At 138, the state change data isanalyzed in connection with making a diagnosis and/or prognosis. At 140,appropriate action is taken in accordance with the analysis.

It is to be appreciated that other methodologies may be employed inaccordance with the subject invention. For example, at 132 the receivedstate change data can be in the form of a bit or flag being set, andsuch information could be transmitted upon the change, or cached orqueued until appropriate to transmit. It is also to be appreciated thatany suitable data format (machine code, binary, hexadecimal, microcode,machine language, flags, bits, XML, schema, fields, . . . ) and/ortransmission protocol/scheme/medium (http, TCP, Ethernet, DSL, optics,RF, Internet, satellite, RF, . . . ) for carrying out thefunctionalities described herein can be employed and such formats andprotocols are intended to fall within the scope of the hereto appendedclaims.

It is to be appreciated that a blackboard scheme may also be employed incertain situations. In the blackboard scheme, an agent or cluster willpost a message or condition to the blackboard along with appropriatesource and context information. Other system components or agents mayquery the blackboard to determine if any relevant information is posted.It is also to be appreciated that an agent registry scheme may also beemployed in certain situations. A registry scheme requires distributedagents to periodically register information such their currentoperation, capabilities, capacities, and plans with a separate resourcefacility. Operating as a “yellow pages” this registry is available toother system agents who require additional facilities or capabilities tomeet current requirements. This registry is also available to assistagents and agent clusters in negotiation and action planning to addressfuture possible scenarios. For example, the registry may be used toestablish a future configuration and operating scenario from a set ofpossible contingency plans that will provide a less disruptive ordangerous configuration in the event a recently detected weakenedcomponent should fail. The weakened component may have indicated itsdegraded state through a broadcast message as described above or byupdating the local cluster register.

Furthermore, a combination of broadcast, polling, blackboard, orregistry update schemes can be employed in connection with the invention(e.g., as part of an optimization scheme) for conveyance of state changeinformation.

Component, device, subsystem, or process health or prognosticinformation may be communicated in an explicit message using thecommunication mechanism and architecture described above. Alternatively,the machinery current condition and prognostic information may beembedded in the communications message. The machinery health informationmay be embedded in particular message segments reserved for machineryhealth information. Diagnostic and prognostic status bits may be definedand used by any intelligent machine on the network. The bits may be setby the intelligent machine based on the machine's continuous healthself-assessment. Alternatively, adjacent intelligent components orcollaborating agents may report another agent or component isineffective in performing its function or perhaps is no longer able tofunction or no longer reachable on the network.

Other schemes for encoding machinery diagnostic and prognostic healthinformation may be employed such as encoding this information in themessage header, or in the text of the message. Encryption schemes thathide the encoded health information may be employed. This can providefor lower message overhead and increase security and messagereliability. Alternatively, the characteristics of the message such asmessage length, time of transmission, frequency of message transmission,or scope of destination may convey device health and/or prognosticinformation.

Instead of or in addition to providing state/event change informationabout itself or the cluster it belongs to, related information regardingfuture states or events may be provided. This information provided mayinclude an array with each element comprised of three or more values.The values for each entry may be the future state or event, theprobability or likelihood of the event occurring and the expected timeor condition in the future that this event may occur with the specifiedcertainty.

It is to be appreciated that although the subject specificationprimarily described the invention within the context of prognosis, theinvention is intended to encompass diagnostics as part of or in additionto performing prognostics.

Various artificial intelligence schemes/techniques/systems (e.g., expertsystems, neural networks, implicitly trained classifiers, explicitlytrained classifiers, belief networks, Bayesian networks, naïve Bayesiannetworks, HMMs, fuzzy logic, data fusion engines, support vectormachines, . . . ) can be employed in connection with making inferencesregarding future states in accordance with the subject invention. Assuch an AI component in accordance with the subject invention canfacilitate taking a probability-based or statistics-based approach toperforming utility-based prognoses in accordance with the subjectinvention. It is to be appreciated that the other embodiments of theinvention can perform automated action based on predicted state viasimple rules-based techniques (e.g., look-up table), for example, tomitigate processing overhead. Moreover, a combination thereof can beemployed as part of an optimization scheme.

Turning to FIGS. 1d -h, the subject invention also contemplates aclosed-loop system that employs prognostics. A prognostics engine can beused to predict future states or events relating to a system. Thepredicted state or events can be, for example, quality of a product,production throughput, possible line failure, machine temperature,bearing failure, order arrival, feed stock quality, etc. The system canemploy such prognostic information to dynamically modify the systemand/or process (e.g., continuously cycling through the prognostics loop)until convergence is achieved with respect to desired predicted futurestate(s). Thus, prognostics in accordance with this aspect of theinvention can be employed as part of an in situ monitoring andmodification scheme to facilitate achieving a desired result. It is tobe appreciated that the state of the system will often dynamicallychange, and the subject embodiment can be employed as part of acontinuous closed-loop system to not only converge on a desired state(including predicted future state), but also to maintain such state, andmitigate the system from entering into an unstable or undesired currentor predicted future state. Thus, the system can serve as aself-diagnosing and correcting system.

A prediction or prognosis can indicate the expected future state of thesystem or possible future states of the system with definedprobabilities based on the likelihood or probability of other outsideinfluencing factors. If the expected future state (or possible futurestates) is acceptable, the system or plant may be monitored andcontrolled to insure the expected state (or one of the possible expectedstates) are realized. If the expected future state is unacceptable(e.g., tank rupture) then configuration or operating changes may bedefined that will put the system state trajectory on a more safe ordesirable path. Since a large suite of more desirable trajectories andfuture state outcomes are possible, the most desirable, greatestbenefit, most valuable, and highest probability states may be selected.A closed loop monitoring and control system will insure the system isprogressing toward the previously selected optimum or most desirablestate. Unexpected disturbances or new factors may cause the system tore-adjust the state trajectory or alter the control as necessary. A goalcan be to define possible or likely future states, select criticalstates to avoid and identify more desirable/optimum states. Thenidentify what may be very slight control changes early to drive specificstate variable(s) on a prescribed (more desirable) trajectory subject toinput constraints and process constraints. A feedback mechanismincluding regular prognostics and control alteration can insure that thesystem in on the correct, more desirable trajectory resulting inachieving the pre-planned, optimal state in the future as described inFIG. 1 i.

RFIDs can also be employed in accordance with a particular aspect of theinvention. The RFIDs, can provide for component tracking and monitoringsuch that the progostics system, for example, as described above canalso participate in tracking and locating devices within a system orprocess and optimize taking automated action in connection therewith.For example, if a portion of a production line is predicted to go downwithin a few seconds, components (produced in part) upstream from theline about to go down can be quickly rerouted by the system as part ofan automated corrective action in accordance with the subject invention.Accordingly, the RFID tags on the components can facilitate quicklyidentifying current and predicted future location of thereof so as tooptimize the above action. It is to be appreciated that any suitablescheme (e.g., global positioning system, RF-based, machine vision,web-based . . . ) can employed with such aspect of the subjectinvention. It is to be appreciated that many conventional GPS-typesystem(s) are limited with respect to indoor tracking, and in suchsituations, wireless based schemes can be employed to determine and/orinfer location of components.

A security component can be employed with prognostics in connection withthe subject invention. The inventors of the herein claimed inventioncontemplate the potential dangers associated with taking automatedaction based on inferred/predicted future state. Critical portions of anetwork, system and/or process can be vulnerable to malicious and/orerroneous action. Accordingly, security measures (e.g., data encryption,user authentication, device authentication, trust levels, SOAPprotocols, public/private keys and protocols, virus control . . . ) canbe employed to mitigate undesired action and/or prognoses beingperformed in connection with the subject invention. Accordingly, schemesfor weighing evidence, data integrity, security, confidence, patternrecognition, etc, can be employed to facilitate that received data andprognoses with respect thereto are accurate and reliable. Any suitablescheme for effecting such measure can be employed in connection with theinvention, and are intended to fall within the scope of the heretoappended claims. Moreover, another aspect of the invention can providefor an override component that prevents a recommended automatic actionbeing taken given the cost of making an incorrect decision (e.g.,turning off power, initiating fire suppression, starting a ballast pump,turning off life support . . . ).

Furthermore, if desired, certain aspects of a system or process can beisolated (e.g., firewall) such that prognostics and automated action inconnection therewith cannot be taken on such isolated section. Forexample, certain tasks may be deemed so critical that only a trusted andauthenticated human can take action in connection therewith. Forexample, on a submarine, HVAC and power control may be deemed socritical that at a certain part of control thereof, automated action isturned over to a human Likewise, such aspects of the subject inventioncan be employed to mitigate undesired chain reactions (e.g., stockmarket crash of 1980s wherein computers flooded the market with sellorders, East Coast blackout of 2003 wherein a substantial portion of anintegrated power grid crashed as part of a load-shedding chain reaction. . . ). However, it is to be appreciated that prognostics in accordancewith the subject invention can facilitate avoidance of entering into achain reaction type situation by making inference at a granular leveland taking remedial action to mitigate a low-level undesired statesituation blossoming into a larger, potentially catastrophic situation.

Accordingly, the invention contemplates performing a utility-basedapproach in connection with a security-based approach to facilitatetaking optimal/appropriate actions given particular state(s) and contextthereof. Furthermore, some critical action such as turning off a pump,may be deemed particularly sensitive and potentially dangerous. Beforethis action is automatically invoked based on prognostics, it may berequired that two or more, independent system components (e.g. agentclusters) may corroborate the expected or potential future state andindependently establish that the optimum course of action is to turn offthe pump or machinery. One of the several corroborating but independentsystem components may be a human.

Another aspect of the subject invention analyzes not only stateinformation with respect to components, but also state information withrespect to extrinsic factors (e.g., ambient temperature, dust,contaminants, pressure, humidity/moisture, vibration, noise, radiation,static electricity, voltage, current, interference (e.g., RF), . . . )that may effect future state of components. Accordingly, by predictingfuture states as to such extrinsic factors and taking action inconnection with controlling such factors, various components can beprotected from entering into undesired future states. For example, manyfailures of machines can be attributed to environmental influences(e.g., contamination) that can contribute to failure of the machine. Bymonitoring and controlling such influences in a dynamic and proactivemanner, machine failure can be mitigated.

Referring to FIG. 1 j, another aspect of the subject invention is toestablish the potential future state of the system given particularoperating scenarios, process runs, or mission scenarios. A suite ofpossible operating conditions can be mapped against the presentcondition of the system and system components to determine the likelyoutcome of possible operating profiles or missions. If the outcome fromsome possible operating scenarios is undesirable (e.g., catastrophicmachinery failure) then this future operating scenario may be avoided.For example, a process run involving a high-temperature and highpressure reaction or military mission over hostile territory of lengthyduration may indicate likely gearbox or engine failure before successfulcompletion. Performing an analysis of the outcome of potential operatingdecisions or “what-if” scenarios can provide a basis for optimizing thedeployment of resources and provide a superior measure of safety,security, and asset optimization.

Yet another aspect of the subject invention provides for remote dataanalysis and prognostics to be performed on a system. Accordingly, datarelating to a system/process can be collected and transmitted (e.g., viathe Internet, wireless, satellite, optical fiber . . . ) to a remoteprognostic engine that analyzes the data and makes inferences as tofuture state of the system (or subset thereof) based in part on thedata. For example, a small facility in a rural location may operatenumerous motors and pumps in a harsh environment not necessarilysuitable for highly sensitive processing components. Accordingly, datacan be gathered at such location, and transmitted in real-time (ordiscrete time) and analyzed at the remote location where the sensitiveprocessing components reside along with databases (e.g., historicaldata, trend data, machine data, solutions data, diagnostic algorithms .. . ) that can facilitate speedy analysis and diagnosis/prognosis ofsystems/machines/processes at the rural location.

FIG. 1k is a high-level diagram illustrating one particular system 150in connection with the subject invention. The system 150 includes aplurality of machines 161 (MACHINE₁ through MACHINE_(N)—N being aninteger) at least a subset of which are operatively coupled in a mannerso as to share data between each other as well as with a host computer170 and a plurality of business components 180. The machines 161 includea respective diagnostic/prognostic component 182 that provides forcollecting and/or generating data relating to historical, current andpredicted operating state(s) of the machines. It is to be appreciatedthat the plurality of machines can share information and cooperate; andis it to be appreciated that the machines do not have to be the same.Furthermore, some of the machines 161 may comprise sub-systems orlower-level components that can have separate sensors, lifetimeestimates, etc. For example a compressor may consist of a motor, pump,pressure chamber, and valves. The motor component may include smartbearings with embedded sensors to predict bearing lifetime.

The predicted operating state(s) of the machine may be determined basedon expected demand or workload or a probabilistic estimate of futureworkload or demand. Similarly, expected environment (e.g., temperature,pressure, vibration, . . . ) information and possible expected damageinformation may be considered in establishing the predicted future stateof the system. Undesirable future states of the system may be avoided ordeferred through a suitable change in the control while achievingrequired operating objectives and optimizing established operational andbusiness objectives. Moreover, it is to be appreciated that datarelating to subsets of the machines can be aggregated so as to providefor data relating to clusters of machines—the cluster data can providefor additional insight into overall system performance and optimization.The clusters may represent sub-systems or logical groupings of machinesor functions. This grouping may be optimized as a collection of processentities. Clusters may be dynamically changed based on changingoperating requirements, machinery conditions, or business objectives.The host computer 150 includes an enterprise resource planning (ERP)component 184 that facilitates analyzing the machine data as well asdata relating to the business concern components 180 (utilitiescomponent 186, inventory component 188, processes component 190,accounting component 192, manufacturing component 194 . . . ). The datais analyzed and the host computer 170 executes various optimizationprograms to identify configurations of the various components so as toconverge more closely to a desired business objective. For example,assume a current business objective is to operate in a just in time(JIT) manner and reduce costs as well as satisfy customer demand. If theinventory component 188 indicates that finished goods inventory levelsare above a desired level, the ERP component 184 might determine basedon data from the utility component 186 and machine components 160 thatit is more optimal given the current business objective to run themachines at 60% rather than 90% which would result in machineryprognostics indicating we may extend the next scheduled maintenance downtime for another four months reducing the maintenance labor and repairparts costs. This will also result in reducing excess inventory over aprescribed period of time as well as result in an overall savingsassociated with less power consumption as well as increasing lifeexpectancy of the machines as a result of operating the machines as areduced working rate.

It is to be appreciated that optimization criteria for machineryoperation can be incorporated into up-front equipment selection andconfiguration activities—this can provide additional degrees of freedomfor operational control and enhanced opportunities for real-timeoptimization.

Maintenance, repair, and overhaul (MRO) activities are generallyperformed separate from control activities. Interaction andcollaboration between these functions are typically limited to the areasof operations scheduling and to a lesser extent in equipmentprocurement—both are concerned with maximizing production throughput ofthe process machinery. Information from MRO systems and machinerycontrol and production systems are related and can provide usefulinformation to enhance the production throughput of process equipment.The subject invention leverages off opportunities realized by closelycoupling machinery health (e.g. diagnostics) and anticipated health(e.g. prognostics) information with real-time automatic control. Inparticular, the closed-loop performance of a system under feedbackcontrol provides an indication of the responsiveness, and indirectly,the health of the process equipment and process operation. Moreimportantly, it is possible to change how the system is controlled,within certain limits, to alter the rate of machinery degradation orstress. Using real-time diagnostic and prognostic information thesubject invention can be employed in connection with altering futurestate(s) of the machinery. Given a current operating state for both themachinery and the process the subject invention can drive the machine(s)160 to achieve a prescribed operating state at a certain time in thefuture. This future operating state can be specified to be an improvedstate than would occur if one did not alter the control based onmachinery health information. Furthermore, the future state achievedcould be optimal in some manner such as machinery operating cost,machinery lifetime, or mean time before failure for example. Theprescribed operating state of a particular machine may be sub-optimalhowever, as part of the overall system 150, the system-wide operatingstate may be optimal with regard to energy cost, revenue generation, orasset utilization.

For example, with reference to Table I below:

TABLE I Direct Line Power - Drive Power - Power Source/Control FlowControl Flow Control Technique with Throttle Valve via Motor Speed FullFlow - Power 1.07 kW 1.13 kW Flow: 75 gpm (flow not restricted) ReducedFlow - Power .881 kW .413 kW Flow: 45 gpm (restricted flow)

The above data exhibits energy utilization from a motor-pump systemunder conditions of full flow and reduced flow. The flow rate conditionsshown are achieved using a variable speed drive to control motor speedand therefore flow rate (column 1) and with a motor running directlyfrom the power line with a throttling valve used to control flow rate(column 2). The estimated energy savings with Drive Power at reducedflow is 0.468 kW—a 53% energy savings in connection with Drive Power.Pumping applications which require operation at various prescribed headPressures, liquid levels, flow rates, or torque/speed values may beeffectively controlled with a variable speed motor drive. The benefitsof using a variable speed motor controller for pump applications arewell established, particularly for pumps that do not operate at fullrated flow all the time. In fact, the variable speed drive used fortesting in connection with the data of Table I has a user-selectablefactory setting optimized for fan and pump applications although theseoptimized settings were not employed for the energy savings reportedherein. The scope of benefits beyond energy savings include improvedmachinery reliability, reduced component wear, and the potentialelimination of various pipe-mounted components such as diverters andvalves and inherent machinery protection such as from over-current orunder-current operation. Pumps which typically operate at or near fullsynchronous speed and at constant speed will not realize the energysavings as we have demonstrated in Table I. Process conditions thatrequire pump operation at different flow rates or pressures (or arepermitted to vary operation within process constraints) are candidatesto realize substantial energy savings as we have shown. If maximumthroughput is only needed infrequently, it may be beneficial to specifythe hydraulic system and associated control to optimize performance overthe complete span of operating modes based on the time spent in eachmode. It will be necessary in this case to specify the duration of timethe hydraulic system is operating at various rating levels coupled withthe throughput and operating cost at each level.

Although machine control is discussed herein primarily with respect tomotor speed, the invention is not to be construed to have controllimited to such. Rather, there are other control changes that can bemade such as for example changing controller gains, changing carrierfrequency in the case of a VFD motor controller, setting current limitson acceleration, etc. The control can be broad in scope and encompassmany simultaneous parameter changes beyond just speed. Moreover, the useof models can be a significant component of control and configurationoptimization. A space of possible operating conditions for selectionthat optimizes a given process or business performance may be determinedby employing a simulation model for example. Modeling techniques canalso serve as a basis for prognostics—thus, a simulation model canencompass process machinery, throughput, energy costs, and business andother economic conditions.

With respect to asset management, it is to be appreciated that thesystem 100 may determine for example that purchasing several smallermachines as compared to a single large machine may be more optimal givena particular set of business objectives.

It is also to be appreciated that the various machines 161 or businesscomponents 180 or a subset thereof can be located remotely from oneanother. The various machines 161 and/or components 180 can communicatevia wireless or wired networks (e.g., Internet). Moreover, the subjectinvention can be abstracted to include a plant or series of plants withwireless or wired networked equipment that are linked via long distancecommunications lines or satellites to remote diagnostic centers and toremote e-commerce, distribution, and shipping locations for dynamiclogistics integrated with plant floor prognostics and control. Thus,optimization and/or asset management in connection with the subjectinvention can be conducted at an enterprise level wherein variousbusiness entities as a whole can be sub-components of a larger entity.The subject invention affords for implementation across numerous levelsof hierarchies (e.g., individual machine, cluster of machines, process,overall business unit, overall division, parent company, consortiums . .. ).

FIG. 2 illustrates operating levels over time of an exemplary pumpsystem. The few, rare excursions at maximum flow result in hydrauliclosses and energy losses during most of the operating time at lower flowrates. Integrating the losses under a peak efficiency curve provides anestimate of aggregate losses (and saving opportunity) for a target pumpapplications. Aggregate pump level usage information is represented in avery concise manner by Frenning, et al. (2001) in a duration diagram.This diagram shows the number of hours per year needed at various flowrates and provides a means to evaluate potential performance and energybenefits through up-front system design and control specification.Beyond these established benefits, there are important novel benefitsassociated with integrating diagnostics and prognostics information withestablished automatic motor control methods as discussed herein.

It is to be appreciated that the subject invention employs highlysophisticated diagnostic and prognostic data gathering, generation andanalysis techniques, and should not be confused with trivial techniquessuch as automatic disconnect based on an excessively high current ortemperature to be integrated diagnostics (e.g., something is wrong) andcontrol (e.g., automatic contact closure). For the purpose ofestablishing an intelligent system for pump applications as describedabove, we do not consider such machinery protection with bang-bang,on-off control to be integrated diagnostics and control. Diagnosticinformation as employed by the subject invention can be informationregarding a condition of system components or operating conditions thatwill accelerate wear and hasten failure of critical system elements. Forexample, information which identifies a level of degradation of abearing element, the degree of insulation capability lost, the amount oftime motor windings were operated at elevated temperature or thatcavitation is occurring is useful diagnostic information. Suchinformation can be combined to automatically alter prescribed controlaction, within allowable limits, to maintain useful operation andpotentially reduce stress and degradation rate(s) of weakenedcomponents. The ultimate effect is to defer, under controlledconditions, eventual machinery failure.

Feedback control for pumping applications will often have one or moreprocess variables such as flow rate, head pressure, or liquid levelsensed by a transducer and converted to a digital signal. This digitizedsignal is then input to a control computer where the sensed digitizedvalue is compared with the desired, setpoint value as discussed ingreater detail infra. Any discrepancy between the sampled value and thesetpoint value will result in a change in the control action to themotor-pump system. The change to the motor-pump system may be a newcommanded valve position for a motor-operated valve or a new commandedsetpoint speed for a variable speed motor application.

Feedback control systems as described above are termed error-nullingprocesses. We may represent the feedback controlled pumping system as alumped parameter linear system. The most general state spacerepresentation of a linear, continuous time dynamical system can beprovided as:

{dot over (x)}=A(t)x(t)+B(t)u(t)   (1)

y(t)=C(t)x(t)+D(t)u(t)

Here x(t) is the state vector representation of the system, u(t) is thevector of real-valued inputs or control variables, and y(t) is thevector of system real-valued outputs. Matrices A, B, C, and D representthe plant or process state transitions, control input transition, stateoutput process, and direct input-output (e.g. disturbances) processrespectively. It is possible to incorporate diagnostic information intothis controller by altering the controller based on assessed equipmenthealth. For example, if the diagnostic analysis indicates that motorwindings are beginning to heat up we may alter the controller to reducethe gain used to determine system input changes. This will result in asystem with less stress on the motor windings but at the expense ofslightly less system response. We may employ other techniques to shiftlosses from weakened components to stronger system elements. If it isdetermined through vibration analysis or current signature analysistechniques that operation is at a critical or resonant frequency, we mayalter system speed to avoid such critical frequencies that mayaccelerate wear of bearing components.

As another example, if we detect that cavitation is occurring in thepump based on computed pump parameters and pump curves, we may reducemotor speed to eliminate the degrading cavitation condition. Inparticular, we may reduce speed to the point that adequate net positivesuction head available (NPSHA) is equal to the net positive suction headrequired (NPSHR). As operating conditions changes and NPSHA increases,then motor speed may be automatically increased to the point thatmaximum flow is one again achieved while NPSHR<=NPSHA. A more detailedexample of an integrated diagnostic system with compensating control isdescribed below in the case study.

It is significant to note that in the absence of downstream transducersfor pressure and speed, the existence of many pumping problems can bedetermined using only sampled motor current. For example, with pumpingsystems, motor speed can be determined from motor current. The existenceof cavitation can be determined from a single phase of motor currentduring pump operation. Such observation is significant since pump curvesare not required to perform this diagnosis and the results arepotentially more accurate since what is being sensed is a specificfeature indicative of cavitation rather than utilizing pressure, flow,and pump nominal curves. Changes in viscosity, chemical composition, andpump geometry such as from wear, will alter the accuracy of the pumpcurves. MCSA techniques promise to be more accurate and less invasivethan more traditional pressure-flow measurements with pump nominaldesign information.

Through various diagnostic means such as described above it is possibleto determine that an undesirable operating state is occurring or thatcertain degraded components will result in early machinery failure.Important benefits are possible by automatically altering the control toavoid the higher-stress operating and control modes or to avoidstressing weakened or degraded components and thereby extend the usefuloperating life of machinery.

Prognostics & Control

Although process optimization has been employed for many years (e.g.dynamic optimization) such as for continuous chemical processingapplications, unique and important benefits are possible by utilizingmachinery diagnostics and prognostic information to prescribe an optimumcontrol action dynamically. The benefits of integrated diagnostics andcontrol may be significantly expanded by utilizing informationdescribing the rate of degradation and remaining useful life ofmachinery under various possible operating conditions. This permitschanging the operating mode to achieve a designated operating lifetime.Alternatively, the control can be specified to minimize energyconsumption and maintenance costs or to maximize revenue generation. Inextreme conditions, the control may specified to achieve performancebeyond the normal operating envelope to protect the environment, avoidcostly losses, or protect worker safety while insuring that failure willnot occur during these extreme operating conditions. Prognostics withcontrol provides the foundation for overall process optimization withregard to objectives such as efficiency, business strategies,maintenance costs, or financial performance.

Implementing variable speed motor control for pumping applications canprovide direct savings in reduced energy consumption as describedherein. Additional benefits are possible by treatingdrive-motor-pump-hydraulics as an integrated system. Combiningindividual efficiency curves of a motor, pump, and drive permitsgenerating a composite system performance profile. This aggregate systemmodel can be used to diagnose the system as an integrated collection ofcoupled elements and to prescribe a preferred operating state of thesystem.

In connection with the subject invention it is proposed to extend thecontrol model for the variable speed motor controller by incorporatingthree additional elements in the control model.

The three elements that augment the control model are:

-   -   Specification of the allowable range of operation    -   Diagnostic & prognostic information, and    -   Specification of optimal system operation, processing objectives        and business objectives

The first element in the control model is the capability to permitoperation within a range of process (state) variables. For example,although a desired (e.g., setpoint) flow may be 100 gpm, however thesystem may effectively run anywhere between 60 gpm and 110 gpm. Thespecification of the allowable range of operation may include datarelated to the sensitivity, accuracy, or marginal nature of theoperating bound. Probabilistic and time-dependency information may alsobe included in the boundary specification.

The second element in the extended control model is information relatingto the health of the process machinery and its operation along withinformation on the future health of the machinery such as rate ofdegradation and remaining useful life. For example, one may determinethat the elevated temperature rise in the motor windings will reduceinsulation life by ½ or that the detected level of cavitation willaccelerate seal failure by 10 fold.

The third element in the extended control model is an analyticrepresentation of the operating objectives of the process or plant alongwith any additional operating constraints. The representation of theoperating objectives of the process provides a quantifiable measure ofthe “goodness of operation” and may include critical performancecriteria such as energy cost and process revenues. This permitsestablishing an objective function that may subsequently be optimizedthrough suitable control changes. Additional operating constraints mayinclude data such as noise level, maximum process completion time. Anobjective function specifying the process and business benefits may beoptimized via dynamic changes in the control action subject to notviolating any of the process operating constraints.

We can utilize established life expectancy models in conjunction withclassical control techniques to control the residual lifetime ofmachinery. For example, crack growth models based on cyclic loadingprovide a probabilistic model that can be embedded in a simulation modelto determine future stress due to vibration, temperature gradient, andpressure. The Forman deterministic crack growth failure model provides abasis for altering the stress and rate of crack growth directly fromchanges in the control. The altered control then provides a quantitativemeasure of the change in crack growth rate. This information can be usedto control the expected remaining lifetime of degraded components andinsure that failure does not occur before a tank is emptied or ascheduled PM or machinery overhaul occurs.

The subject invention's focus of prognostics and distributed controlwill enable future plant operations to be based on proactive operationrather than reactive problem solving. Device alerts from remoteintelligent devices can warn of future potential problems giving timefor appropriate remedial or preventive action. Embedding operationalobjectives and plant performance metrics into an automateddecision-making system can permit a high degree of machinery reliabilityand avoid the unexpected process failures that impact quality and reduceyields. Integrating prognostic information with automatic, real-timedecision making provides a basis for dynamic optimization and providesunique, important benefits due to optimized plant operation.

Dynamic Optimization

Given that permissible operating modes have been suitably defined, andestablished a means to project into the future possible or probableoperating states, and a criterion for judging preferred or optimalperformance the problem can be formulated as a classical optimal controlproblem.

For example, if the operating objective is to minimize energy cost pergallon pumped then the objective function will include flow information,cost per kWh, and motor-drive power consumed. Dynamic changes can bemade to both the motor speed and drive internal parameters to optimizethe cost per gallon pumped subject to previously defined processconstraints. It is significant to note that the operating example abovewill result in the least energy cost per gallon pumped; however, it mayalso result in accelerated wear or thermal degradation of criticalmachinery components. A more comprehensive operational model andobjective function may incorporate these additional parameters ifrequired. Additional parameters may include information such as expectedfailure rate and failure cost for different operating modes, machinerylifetime and capital replacement costs, and the impact on otherconnected machines and processes such as valves, piping, and otherprocess machines.

One exemplary aspect of the subject invention establishes a controlmethod that will support decision making at each decision time intervalor control iteration loop. One principle of dynamic programmingspecifies that if the system is at some intermediate point on an optimalpath to a goal then the remainder of the path must be on an optimal pathfrom the intermediate point to the goal. This permits making optimumchoices of the control variable, u(t), at time t that by onlyconsidering the need to drive the system from state x(t) to x(t_(f)),the final state of the system. This approach provides an efficienttechnique for sequential decision making while insuring that thecomplete system trajectory will be optimum from time t0 to t_(f) and wedo not need to consider all possible control options at every decisionpoint simultaneously.

The optimization problem can be formulated as:

Min J=S(x(t _(f)), t _(f))+∫_(to) ^(tf) L(x(t), u(t), t)dt   (2)

-   -   Subject to f(x(t), {dot over (x)}(t), y(t), u(t))=0 where t ∈        [t₀, t_(f)]        with defined initial conditions, time constraints, control        variable and state variable constraints. Here J represents an        objective function value to be minimized (or maximized). S and L        are real-valued functions with S representing cost penalty due        to the stopping error at time t_(f) (e.g. wasted fluid not        pumped or discarded useful life in replaced equipment). L        represents the cost or loss due to transient errors in the        process and the cost of the control effort during system        operation.

For example, if the value of the stopping cost function is set at S=0and L=u^(t)u then:

Min J=∫ _(to) ^(tf) u ^(t) u dt   (3)

Equation 3 is a measure of the control effort or energy expended for aprocess operating from time t_(o) to time t_(f). This is termed theleast-effort problem and in the case of a drive-motor-pumping system,results in completing a process segment (e.g. emptying a tank) at thelowest possible energy cost.

When J is differentiable, gradient search techniques can be employed tocompute the desired change in control, u(t), that moves J closer to theminimum (or maximum value). The concept of the gradient is significantin that the change in the objective function we obtain from a suitablecontrol u(t) is proportional to the gradient, grad(J). This provides aspecification for the change in u needed to move J closer to theoptimum. If J is convex then local optimum values are not much ofconcern and any optimum value obtained is a global optimum. Thisformulation permits a step-by-step evaluation of the gradient of J andthe selection of a new control action to drive the system closer to anoptimum.

The gradient search technique, also called the method of steepest decentis illustrated graphically in FIG. 3. Here each arrow represents a newcontrol decision in the quest to realize a minimum value for theobjective function, J. The specification of the optimal performancemetric, J, can incorporate information beyond energy utilization,maintenance cost, or longevity of operation. For example, it is possibleto also formulate J to include strategic business information and assetvalue information. In this manner selecting the sequence of optimalcontrol actions u(t) to optimize J will drive the system to achieveoptimum utilization of the assets involved.

Asset Optimization

The specification of the optimum operation of plant equipment describedabove provides a flexible platform to incorporate various business andoperational factors. It is possible to include the cost of maintenancefor various failure modes, replacement and installation costs,maintenance strategies, cost for scrap, re-work, line-restarting, andrevenue generation from the specified machinery. This permits thegeneration and implementation of optimal asset lifetime managementpolicies across critical plant assets. The operational success of thisapproach requires an effective Asset Register base, observability of keystate variables, and viable process and component models. Theutilization of open, industry standards for asset registry providesimportant capabilities for integrating operating information across amanufacturing plant and even across facilities. More recent developmentshave resulted in an Open Systems Architecture for Condition-BasedMaintenance that provides a framework for real-time integration ofmachinery health and prognostic information with decision supportactivities. This framework spans the range from sensors input todecision support—it is open to the public and may be implemented in aDCOM, CORBA, or HTTP/XML environment.

Often complex business and operational decisions are difficult toincorporate into a single, closed-form objective function. In this case,operating decision and control objectives may be decomposed into a suiteof sub-problems such that when taken together, the overall, more complexproblem is solved. For example, a process can be decomposed into apumping process, chemical reaction, and storage/batch transport problem.These decompositions can be treated as individual sub-problems andoptimize each of these subject to boundary or interaction constraintsbetween each sub-problem. Alternatively, the decomposed problem can betreated as a collection of coupled decision and seek an optimum thatbalances possibly conflicting objectives and establishes a compromisedecision or control which is in some sense optimally global. Forexample, an industry-wide drive to improve capital equipment utilizationand enhance RONA values may be in conflict with reducing maintenancecosts and maximizing revenue generation per energy unit consumed.Established techniques for solving coupled and un-coupled optimizationcan be employed to facilitate overall asset optimization. Thecompatibility of control strategies with maintenance and schedulingstrategies provides new opportunities to optimize assets utilization.Automation control actions may automatically be initiated, whichreinforce and drive toward strategic business objectives established bymanagement. In accordance with another particular example, an assetoptimization system can continually monitor energy costs via theInternet and dynamically change machinery operation based on new energycosts to maximize revenue generation. If energy costs becomesubstantially high then the criteria for energy-efficient operation canovertake the optimization criteria of maximizing production throughput.

Real Options Analysis as a New Economic Tool Linking CBM Investments toBusiness Strategy

In connection with machine and business state prognostics, assetmanagement and optimization in accordance with the subject invention, itis to be appreciated that preventing unexpected equipment failures canprovide important operational and economic benefits. Using real optionspricing to provide a more accurate value of deferring machinery repairor altering the control strategy. One aspect of the subject inventionprovides for automatically checking the availability, cost, andperformance specifications of new components to replace healthycomponent. Swapping out old, less efficient components with new, moreefficient components permits further optimizing process operation andoptimizing overall asset utilization.

The asset optimization program in connection with the subject inventionfor example could launch a crawler or spider to search for potentialreplacement components across the Internet. The asset optimizationsystem can for example continually monitor energy costs via the Internetand dynamically change machinery operation based on new energy costs tomaximize revenue generation. If energy costs become high enough then thecriteria for energy-efficient operation will overtake the optimizationcriteria of maximizing production throughput. Machinery failureprevention can be enhanced by implementing a condition-based maintenance(CBM) system with on-line, continuous monitoring of critical machinery.An economic analysis required to justify CBM acquisitions often followsa model used to evaluate other plant acquisitions. However, traditionalmachinery acquisition valuation methods do not adequately capture theoperational and strategic benefits provided by CBM systems.

A financial model derived from options in financial markets (e.g. putsand calls on shares or currencies) is proposed to facilitate capturingunique and important benefits of CBM systems. In particular, a CBMsystem inherently provides future decision and investment optionsenabling plant personnel to avoid a future failure by making thesesubsequent investments (exercising the option). Future options enabledby an initial CBM investment provide economic benefits that aredifficult to capture with traditional capital asset pricing models. Realoptions valuation methods are designed to capture the benefits of futureinvestment and strategic options such as those enabled by a CBM system.Augmenting existing economic analysis methods with an option valuepricing model can capture, in financial terms, the unique and importantbusiness benefits provided by CBM investments.

New developments in condition based monitoring algorithms, sensors,communications, and architectures promise to provide new opportunitiesfor diagnostics and prognostics. CBM systems often require anincremental investment beyond what is needed for basic manufacturing andautomation equipment. The acquisition of condition-based maintenancesystems and components must compete with other acquisition requests toobtain capital from a limited pool of available funds. The costsassociated with implementing a CBM system are often easy to obtainalthough they may have many components such as development, purchase,installation, support, and calibration. However, it has traditionallybeen difficult to accurately capture the benefits associated with a CBMinvestment. Augmenting existing investment analysis methods with realoption valuation methods may provide a more accurate economic picture ofthe benefits from a CBM investment opportunity. Investment decisions aretypically based on a traditional economic analysis of the fundingopportunities available. Traditional funding models such the capitalasset pricing model (CAPM) make assumptions regarding the investmentrequired over time and the expected financial return over time. Thesecash flows are brought back to a net present value (NPV) level using anaccepted discounting method and rate. The discount rate is chosen toaccount for the cost of capital and the inherent risk in the project.The investment analysis typically provides a basis for a go / no-godecision on resource allocation. Once approved, the funded projectproceeds with cash flow proceeding as prescribed in the project plan. Inthis respect, many plant acquisition projects may be considered passive.

A significant and unique characteristic of a CBM investment is thesubsequent operational and investment options it provides management. ACBM system does not inherently prevent a failure or automatically reducemaintenance costs. A CBM system provides the essential information thatpermits avoiding a failure or for minimizing maintenance or repaircosts. Realizing the benefits enabled by a CBM system requires activedecision making to initiate the indicated repair, operating changes, oracquisitions. Similar to financial investment models such as put andcall, a CBM investment does not prevent a failure or automaticallygenerate profit, it affords an option to take action sometime in thefuture (exercise an option) to realize a financial or operationalbenefit. The option to make future decisions may be captured in aneconomic model derived from financial investment futures. Thistechnique, called real options valuation, is directed at establishing aneconomic value of an investment that includes the benefits (and costs)derived from potential future investments. The potential futureinvestment options are enabled by the initial investment and they may bedeferred, exercised, or canceled at some time in the future when moreinformation in known. In this sense, real options valuation takes intoaccount the dynamic and active role of management over the life of theinvestment.

The subject invention can augment the traditional economic valuationmethods used for plant acquisitions with results from a real optionsvaluation to establish the value of a CBM investment. Condition basedmaintenance systems provide information essential for establishingeffective reliability centered maintenance programs. Informationregarding the degree of machinery degradation, a diagnosis of an earlystage fault, and prognostics information such as remaining useful lifeenable plant maintenance and operations personnel to take action tominimize maintenance expenses and operations impact. A real optionsapproach to evaluating investments in machinery monitoring anddiagnostic systems may provide insight into the future value associatewith subsequent linked investment options. Investment in an initial CBMsystem for example can provide future, more informed options to furtherexpand the core CBM system or to integrate the system into otherbusiness information systems. Alternatively, information from theinitial CBM system can enable other operational investments thatotherwise would not be available. For example, a CBM system may providea basis for accelerating periodic maintenance, or may prescribereplacing equipment just before failure and minimizing the amount ofremaining useful life that is discarded. Information from the CBM systemmay also provide valuable information on when to exercise the upgrade orreplacement option.

The aforementioned examples and discussion are simply to convey thenumerous advantages associated with the subject invention. It is to beappreciated that any suitable number of components and combinationthereof can be employed in connection with optimizing the overall system100 in accordance with the present invention. Moreover, as a result ofthe large number of combinations of components available in connectionwith the subject invention some of the combinations will have knowncorrelations while there may exists other correlations not readilyapparent but yet still have an influence in connection with optimizationof the system 100. Accordingly, in connection with one particular aspectof the invention data fusion can be employed in situations in order totake advantage of information fission which may be inherent to a process(e.g., vibration in the machine 110) relating to sensing a physicalenvironment through several different sensor modalities. In particular,one or more available sensing elements may provide a unique window intothe physical environment where the phenomena to be observed is occurring(e.g., in the motorized system and/or in a system of which the motorizedpumping system is a part). Because the complete details of the phenomenabeing studied (e.g., detecting the operating state of the system orcomponents thereof) may not be contained within a single sensing elementwindow, there is information fragmentation which results from thisfission process. These information fragments associated with the varioussensing devices may include both independent and dependent components.

The independent components may be used to further fill out (or span) theinformation space and the dependent components may be employed incombination to improve the quality of common information recognizingthat all sensor data may be subject to error and/or noise. In thiscontext, data fusion techniques employed in the ERP system 132 mayinclude algorithmic processing of sensor data in order to compensate forthe inherent fragmentation of information because a particular phenomenamay not be observed directly using a single sensing element. Thus, datafusion provides a suitable framework to facilitate condensing,combining, evaluating and interpreting the available sensed informationin the context of the particular application. It will further beappreciated that the data fusion may be employed in the diagnostics andprognostic component 132 in order to employ available sensors to inferor derive attribute information not directly measurable, or in the eventof sensor failure.

Thus, the present invention provides a data fusion framework andalgorithms to facilitate condensing, combining, evaluating andinterpreting various sensed data. The present invention also facilitatesestablishing a health state of a system, as well as for predicting oranticipating a future state of the machine(s) 110 and/or the system 100(e.g., and/or of a sub-system of which the motorized pump system 110 isa part). The data fusion system may be employed to derive systemattribute information relating to any number of attributes according tomeasured attribute information (e.g., from the sensors) in accordancewith the present invention. In this regard, the available attributeinformation may be employed by the data fusion system to deriveattributes related to failed sensors, and/or to other performancecharacteristics of the machine(s) 110 and/or system 100 for whichsensors are not available. Such attribute information derived via thedata fusion may be employed in generating a diagnostics signal or data,and/or in performing control functions in connection therewith.

In another example, a measured attributes may comprise flow and pressuresignals obtained from sensors associated with the machine 110 (e.g.,pump), wherein the diagnostics system 132 provides a diagnostics signalindicative of pump cavitation according to measured flow and pressuresignals. The invention thus provides for health indications relating tocomponent conditions (e.g., wear, degradation, faults, failures, etc.),as well as those relating to process or systems conditions, such ascavitation in the pump 110. The diagnostics system 132 may comprise aclassifier system, such as a neural network, detecting pump cavitationaccording to the measured flow and pressure signals, which may beprovided as inputs to the neural network. The cavitation indication inthe resulting diagnostics signal or data may further be employed tomodify operation of the machine 110 and/or system 100, for example, inorder to reduce and/or avoid such cavitation. Thus, an appropriatecontrol signal may be provided by a controller to a motor drive inconnection with the pump 110 in order to avoid anticipated cavitation,based on the diagnostics signal (e.g., and/or a setpoint), whereby theservice lifetime of one or more system components (e.g., pump) may beextended.

In another related example, cavitation (e.g., actual or suspected) inthe pump 110 may be detected via measured (e.g., or derived) currentsignal measurements, for example, via a sensor. The diagnostics system132 in this instance may provide a diagnostics signal indicative of pumpcavitation according to the measured current. In order to detectcavitation using such current information, the diagnostics system 132may employ the neural network to synthesize a change in condition signalfrom the measured current. In addition, the diagnostics system 132 mayfurther comprise a preprocessing portion (not shown) operatively coupledto the neural network, which conditions the measured current prior toinputting the current into the neural network, as well as a postprocessing portion operatively coupled to the neural network todetermine whether the change in condition signal is due to a faultcondition related to a motorized system driving the pump 110. In thisregard, the post processing portion may comprise a fuzzy rule basedexpert system. In addition, the diagnostics system 132 may detect one ormore faults relating to the operation of the pump 110 and/or one or morefaults relating to the operation of a motor driving the pump 110according to the measured current.

Other faults may be detected and diagnosed using the diagnostics andcontrol system 132 of the invention. For instance, the diagnosticssystem 132 may be adapted to obtain a space vector angular fluctuationfrom a current signal (e.g., from a current sensor) relating tooperation of the motor driving the pump, and further to analyze thespace vector angular fluctuation in order to detect at least one faultin the motorized system. Such faults may include, for example, statorfaults, rotor faults, and/or an imbalance condition in the power appliedto the motor in the motorized system.

In this situation, the diagnostics/prognostic system 132 may obtain acurrent signal associated with the motor from the sensor, and calculatea space vector from the current signal. The diagnostics/prognosticsystem 132 determines a space vector angular fluctuation from the spacevector, and analyzes the space vector angular fluctuation in order todetect one or more faults associated with the motor driving the pump110. For instance, first, second, and third phase current signalsassociated with the motorized system may be sampled in order to obtainthe current signal, and corresponding first, second, and third phasespace vectors may be computed in the diagnostics/prognostic system 132.

A resulting space vector may then be calculated, for example, by summingthe first, second, and third phase space vectors. Thediagnostics/prognostic system 132 may then compare the space vector witha reference space vector, wherein the reference space vector is afunction of a constant frequency and amplitude, and compute angularfluctuations in the space vector according to the comparison, in orderto determine the space vector angular fluctuation. Thediagnostics/prognostic system 132 then performs frequency spectrumanalysis (e.g., using an FFT component) of the space vector angularfluctuation to detect faults associated with the motorized system. Forexample, motor faults such as rotor faults, stator faults, and/orunbalanced supply power associated with the pump motor may beascertained by analyzing the amplitude of a first spectral component ofthe frequency spectrum at a first frequency, wherein thediagnostics/prognostic system 132 may detect fluctuations in amplitudeof the first spectral component in order to detect one or more faults orother adverse conditions associated with the motorized system. In thisregard, certain frequencies may comprise fault related information, suchas where the first frequency is approximately twice the frequency ofpower applied to the motor driving the pump. Alternative to generating afull spectrum, the diagnostics/prognostic system 132 may advantageouslyemploy a Goertzel algorithm to extract the amplitude of the firstspectral component in order to analyze the amplitude of the firstspectral component. The diagnostics/prognostic signal indicating suchmotor faults may then be employed by a controller to modify operation ofthe pumping system 110 to reduce or mitigate such faults. The abovediscussion in connection with FIG. 1 was presented at a high-level—FIGS.9 and 20 should be referenced in connection with details regarding themotor, drivers, sensors, controllers, etc.

FIG. 4 illustrates an aspect of the subject invention wherein at least asubset of the machines or components are represented via intelligentsoftware agents. For example, each of the respective machines 110 (FIG.1a ) can be represented by respective intelligent agents (MACHINE AGENT₁through MACHINE AGENT_(N)—N being an integer), and various businessconcerns represented by respective agents (e.g., BUSINESS AGENT₁ throughBUSINESS AGENT_(M)—M being an integer). The intelligent agents can besoftware models representative of their various physical or softwarecounterparts, and these agents can serve as proxies for theircounterparts and facilitate execution of various aspects (e.g., machineor component interaction, modification, optimization) of the subjectinvention. The agents can be designed (e.g., appropriate hooks,interfaces, common platform, schema, translators, converters . . . ) soas to facilitate easy interaction with other agents. Accordingly, ratherthan executing an optimization algorithm for example on a respectivedevice directly, such algorithms can be first executed on the respectiveagents and than once the system 100 decides on an appropriate set ofmodifications the final modifications are implemented at the agentcounterparts with the agents carrying the instructions for suchmodifications.

The proliferation of distributed computing systems and enhancedprognostic, control, and optimization techniques provides via thesubject invention for changing the landscape of industrial automationsystems. The aforementioned framework complements technical capabilitiesfor asset optimization via an agent based representation. Agents may beconsidered autonomous, intelligent devices with local objectives andlocal decision making. These agents however can be part of a largercollection of agents and possess social and collaborative decisionmaking as well. These capabilities permit localized, distributed agentsto collaborate and meet new, possibly unforseen operational conditions.In addition, through collaboration, some agents may choose to operate ina sub-optimal mode in order to achieve some higher level objective suchas asset optimization, process safety, or overall process energyoptimization.

FIG. 5 illustrates a representative belief network 500 that can be areused to model uncertainty in a domain in connection with the subjectinvention. The term “belief networks” as employed herein is intended toencompass a whole range of different but related techniques which dealwith reasoning under uncertainty. Both quantitative (mainly usingBayesian probabilistic methods) and qualitative techniques are used.Influence diagrams are an extension to belief networks; they are usedwhen working with decision making. Belief networks are employed todevelop knowledge based applications in domains which are characterizedby inherent uncertainty. A problem domain is modeled as a set of nodes510 interconnected with arcs 520 to form a directed acyclic graph asshown in FIG. 5. Each node represents a random variable, or uncertainquantity, which can take two or more possible values. The arcs 520signify the existence of direct influences between the linked variables,and the strength of each influence is quantified by a forwardconditional probability.

Within the belief network the belief of each node (the node'sconditional probability) is calculated based on observed evidence.Various methods have been developed for evaluating node beliefs and forperforming probabilistic inference. The various schemes are essentiallythe same—they provide a mechanism to propagate uncertainty in the beliefnetwork, and a formalism to combine evidence to determine the belief ina node. Influence diagrams, which are an extension of belief networks,provide facilities for structuring the goals of the diagnosis and forascertaining the value (the influence) that given information will havewhen determining a diagnosis. In influence diagrams, there are threetypes of node: chance nodes, which correspond to the nodes in Bayesianbelief networks; utility nodes, which represent the utilities ofdecisions; and decision nodes, which represent decisions which can betaken to influence the state of the world. Influence diagrams are usefulin real world applications where there is often a cost, both in terms oftime and money, in obtaining information.

An expectation maximization (EM) algorithm is a common approach forlearning in belief networks. In its standard form it does not calculatethe full posterior probability distribution of the parameters, butrather focuses in on maximum a posteriori parameter values. The EMalgorithm works by taking an iterative approach to inference learning.In the first step, called the E step, the EM algorithm performsinference in the belief network for each of the datum in the dataset.This allows the information from the data to be used, and variousnecessary statistics S to be calculated from the resulting posteriorprobabilities. Then in the M step, parameters are chosen to maximize thelog posterior log P(T|D,S) given these statistics are fixed. The resultis a new set of parameters, with the statistics S which we collected areno longer accurate. Hence the E step must be repeated, then the M stepand so on. At each stage the EM algorithm guarantees that the posteriorprobability must increase. Hence, it eventually converges to a localmaxima of the log posterior.

FIG. 6 illustrates an aspect of the invention in which the invention isemployed as part of a distributed system 600 rather than via a hostcomputer (FIG. 1a ). Thus, the various components in the system 600share processing resources and work in unison and/or in subsets tooptimize the overall system 600 in accordance with various businessobjectives. It is to be appreciated that such distributed system canemploy intelligent agents (FIG. 2) as described supra as well as beliefnetworks (FIG. 5) and the ERP components 132 (FIG. 1a ) and data fusiondescribed above in connection with the system 100. Rather than some ofthese components (ERP, data fusion) being resident on a single dedicatedmachine or group of machines, they can be distributed among any suitablecomponents within the system 600. Moreover, depending on which threadson being executed by particular processors and the priority thereof, thecomponents may be executed by a most appropriate processor or set ofprocessors given the state of all respective processors within thesystem 600.

FIG. 7 illustrates another aspect of the subject invention wherein theinvention is implemented among the respective machines 710 in connectionwith optimizing use thereof. For example, the diagnostic/prognosticcomponents 732 can exchange and share data so as to schedule maintenanceof a particular machine, or load balance.

Retuning back to FIG. 1 a, the present invention can also be employed inconnection with asset management. Typically diagnostics activities formany industrial and commercial organizations are conducted separate fromcontrol and process operation activities. In addition, the interface toacquire needed maintenance and repair components is often done manually.Similarly, capital acquisition of replacement equipment is alsoperformed in a manual, batch, off-line manner. Equipment acquisitiondecisions are often made with a separate economic analysis includingpricing analysis and consideration for capital funding available. It isdifficult to incorporate dynamic operational data such as efficiency,reliability, and expected maintenance cost into this analysis. Thegrowing presence of e-commerce and computer-accessible acquisitioninformation is rarely utilized by computer systems. Instead, thesee-commerce systems are often accessed by a human. The subject inventionincludes an optimization function that facilitates realization ofmaximum revenue from an industrial machine while mitigating catastrophicfailure. Machinery operation can be altered as needed to run lessefficiently or noisier as needed to maintain useful machinery operation.

Thus the subject invention integrates the aforementioned optimizationfunctionality with asset management and logistics systems such ase-commerce systems. Such tightly integrated approach can enable aprocess to predict a failure, establish when a replacement componentcould be delivered and installed, and automatically alter the control toinsure continued operation until the replacement part arrives. Forexample, a needed replacement part could automatically be ordered anddynamically tracked via the Internet to facilitate continued operation.Alterations in the control could automatically be made based on changesin an expected delivery date and prognostic algorithms results. Forexample, a prognostic algorithm could determine a drive-end bearingsystem has degraded and has perhaps 500 operating hours left at thecurrent speeds, loads, and temperatures. The correct needed replacementbearing could be automatically ordered via an e-commerce web site (e.g.PTPlace) and shipment tracked until the part arrived. The control may beautomatically altered to extend the useful life of the bearing asrequired (e.g. reducing speed by ½ doubles the bearing life). Delays inreceiving the needed replacement could cause the part to be ordered fromanother source and the control dynamically altered as needed.Maintenance could be scheduled to replace the part to coincide with thepart arrival.

In the case of excessive maintenance costs, the optimization programcould determine that continually replacing failing components is notlonger an optimum strategy and could perform an economic analysis on anew more reliable component or a new machine. The new machine couldprovide a far more optimum solution than continually running in adegraded condition and replacing individual components. The newreplacement machine (e.g. a motor) could be automatically ordered andscheduled to swap out the older, high-maintenance item. Optimizationtechniques that optimize the design and selection of components could beintegrated with real-time dynamic optimization and integrated withinternet-based product information and ordering information to provide asuperior level of process optimization as compared to conventional assetmanagement schemes.

In view of the exemplary systems shown and described above,methodologies that may be implemented in accordance with the presentinvention will be better appreciated with reference to the flow diagramof FIG. 8. While, for purposes of simplicity of explanation, themethodology is shown and described as a series of blocks, it is to beunderstood and appreciated that the present invention is not limited bythe order of the blocks, as some blocks may, in accordance with thepresent invention, occur in different orders and/or concurrently withother blocks from that shown and described herein. Moreover, not allillustrated blocks may be required to implement the methodology inaccordance with the present invention.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more components. Generally, program modules include routines,programs, objects, data structures, etc. that perform particular tasksor implement particular abstract data types. Typically the functionalityof the program modules may be combined or distributed as desired invarious embodiments.

FIG. 8 is a high-level flow diagram depicting one particular methodology800 in connection with facilitating optimizing an industrial automationsystem in accordance with the subject invention. At 810, data relatingto machine diagnostics or prognostics is received. The data can becollected from a historical database, collected in situ for example fromoperation of the various machines, collected via various sensingdevices, and generated via analyzing the aforementioned collected data.The generated data can also relate to future predicted states of therespective machines and/or with respect to clusters of the machines.

The data can be obtained for example via measuring an attributeassociated with a motorized system (e.g., motorized pump, fan, conveyorsystem, compressor, gear box, motion control device, screw pump, andmixer, hydraulic or pneumatic machine, or the like). The measuredattribute may comprise, for example, vibration, pressure, current,speed, and/or temperature associated with the motorized system. The datacan comprise data relating to the health of the motorized systemaccording to the measured attribute. For example, diagnostics data canbe generated which may be indicative of the diagnosed motorized systemhealth, whereby the motorized system is operated according to a setpointand/or the diagnostics data generated. The provision of the diagnosticsdata may comprise, for example, obtaining a frequency spectrum of themeasured attribute and analyzing the frequency spectrum in order todetect faults, component wear or degradation, or other adverse conditionin the motorized system, whether actual or anticipated. The diagnosismay further comprise analyzing the amplitude of a first spectralcomponent of the frequency spectrum at a first frequency.

In order to provide the diagnostics data, the invention may provide themeasured attribute(s) to a neural network, an expert system, a fuzzylogic system, and/or a data fusion component, or a combination of these,which generates the diagnostics signal indicative of the health of themotorized system. For example, such frequency spectral analysis may beemployed in order to determine faults or adverse conditions associatedwith the system or components therein (e.g., motor faults, unbalancedpower source conditions, etc.). In addition, the diagnosis may identifyadverse process conditions, such as cavitation in a motorized pumpingsystem.

At 820 data relating to various business concerns (e.g., inventory,revenue, marketing, accounting, utilities, cash flow, missionstatements, manufacturing, logistics, asset management, layout,processes . . . ) is received and/or generated. Such data can begathered for example from various business software packages, manually,spreadsheets, etc. Moreover, some of the data may be generated viaemployment of artificial intelligence systems (e.g., neural networks,belief networks, fuzzy logic systems, expert systems, data fusionengines, combination thereof).

At 830 and 840, the data is analyzed in connection with optimizationsoftware that analyzes the machine data as well as the business concerndata. Such analysis can include searching for and identifyingcorrelations amongst the data, trend analysis, inference analysis, datamining, data fusion analysis, etc. in an effort to identify schemes forreorganizing, restructuring, modifying, adding and/or deleting thevarious machine and business components so as to facilitate optimizingthe overall business system or method in accordance with identifiedbusiness objective(s).

At 850, a determination is made as to whether component or systemreconfiguration may result in convergence toward optimization. If YES,the system is reconfigured in a manner coincident with a predictedconfiguration expected to achieve a more desired end result. If, NO, theprocess returns to 810.

At 860, a determination is made as to whether the system has beenoptimized. If NO, the process returns to 640. If YES, the processreturns to 810.

The following discussion with reference to FIGS. 9-20 providesadditional detail as to exemplary systems and methods for collecting andanalyzing machine data in connection with the subject invention. It isto be appreciated that such discussion is merely provided to easeunderstanding of the subject invention, and not to limit the inventionto such systems and methods. In FIG. 9, an exemplary motorized pumpsystem 902 is illustrated having a pump 904, a three phase electricmotor 906, and a control system 908 for operating the system 902 inaccordance with a setpoint 910. While the exemplary motor 906 isillustrated and described herein as a polyphase synchronous electricmotor, the various aspects of the present invention may be employed inassociation with single-phase motors as well as with DC and other typesof motors. In addition, the pump 904 may comprise a centrifugal typepump, however, the invention finds application in association with otherpump types not illustrated herein, for example, positive displacementpumps.

The control system 908 operates the pump 904 via the motor 906 accordingto the setpoint 910 and one or more measured process variables, in orderto maintain operation of the system 902 commensurate with the setpoint910 and within allowable process operating ranges specified in setupinformation 968, supplied to the control system 908 via a user interface911. For example, it may be desired to provide a constant fluid flow,wherein the value of the setpoint 910 is a desired flow rate in gallonsper minute (GPM) or other engineering units. The setup information 968,moreover, may comprise an allowable range of operation about thesetpoint 910 (e.g., expressed in GPM, percentage of process variablespan, or other units), and allowable range of operation for otherprocess and machinery parameters such as temperature, pressure, or noiseemission, wherein the control system 908 may operate the system 902 atan operating point within the allowable range.

Alternatively or in combination, setup information, setpoints, and otherinformation may be provided to the control system 908 by a user 912 viaa computer 913 operatively connected to a network 914, and/or bywireless communications via a transceiver 915. Such information may beprovided via the network 914 and/or the wireless communicationstransceiver 915 from a computer (e.g., computer 913) and/or from othercontrollers such as a programmable logic controller (PLC, not shown) ina larger process, wherein the setpoint 910, setup information, and/orone or more economic values 916 (e.g., related to or indicative ofenergy costs, which may vary with time, peak loading values, and currentloading conditions, material viscosity values, and the like) areprovided to the control system 908, as illustrated and described ingreater detail hereinafter. The control system 908, moreover, mayinclude a modem 917 allowing communication with other devices and/orusers via a global communications network, such as the Internet 918,whereby such setpoint, setup, performance, and other information may beobtained or provided to or from remote computers or users. In thisregard, it will be appreciated that the modem 917 is not strictlyrequired for Internet or other network access.

The pump 904 comprises an inlet opening 920 through which fluid isprovided to the pump 904 in the direction of arrow 922 as well as asuction pressure sensor 924, which senses the inlet or suction pressureat the inlet 920 and provides a corresponding suction pressure signal tothe control system 908. Fluid is provided from the inlet 920 to animpeller housing 926 including an impeller (not shown), which rotatestogether with a rotary pump shaft coupled to the motor 906 via acoupling 928. The impeller housing 926 and the motor 906 are mounted ina fixed relationship with respect to one another via a pump mount 930,and motor mounts 932. The impeller with appropriate fin geometry rotateswithin the housing 926 so as to create a pressure differential betweenthe inlet 920 and an outlet 934 of the pump. This causes fluid from theinlet 920 to flow out of the pump 904 via the outlet or discharge tube934 in the direction of arrow 936. The flow rate of fluid through theoutlet 934 is measured by a flow sensor 938, which provides a flow ratesignal to the control system 908.

In addition, the discharge or outlet pressure is measured by a pressuresensor 940, which is operatively associated with the outlet 934 andprovides a discharge pressure signal to the control system 908. It willbe noted at this point that although one or more sensors (e.g., suctionpressure sensor 924, discharge pressure sensor 940, outlet flow sensor938, and others) are illustrated in the exemplary system 902 as beingassociated with and/or proximate to the pump 904, that such sensors maybe located remote from the pump 904, and may be associated with othercomponents in a process or system (not shown) in which the pump system902 is employed. In this regard, other process sensors 941 may beconnected so as to provide signals to the control system 908, forexample, to indicate upstream or downstream pressures, flows, or thelike. Alternatively, flow may be approximated rather than measured byutilizing pressure differential information, pump speed, fluidproperties, and pump geometry information or a pump model. Alternativelyor in combination, inlet and/or discharge pressure values may beestimated according to other sensor signals (e.g., 941) and pump/processinformation.

It will be further appreciated that while the motor drive 960 isillustrated in the control system 908 as separate from the motor 906 andfrom the controller 966, that some or all of these components may beintegrated. Thus, for example, an integrated, intelligent motor may beprovided integral to or embedded with the motor 906, to include themotor drive 960 and the controller 966. Furthermore, the motor 906 andthe pump 904 may be integrated into a single unit (e.g., having a commonshaft wherein no coupling 928 is required), with or without an integralcontrol system (e.g., control system 908, comprising the motor drive 960and the controller 966) in accordance with the invention.

The control system 908 further receives process variable measurementsignals relating to pump temperature via a temperature sensor 942,atmospheric pressure via a pressure sensor 944 located proximate thepump 904, motor (pump) rotational speed via a speed sensor 946, andvibration via sensor 948. Although the vibration sensor 948 isillustrated and described hereinafter as mounted on the motor 906,vibration information may, alternatively or in combination, be obtainedfrom a vibration sensor mounted on the pump 906 (not shown). The motor906 provides rotation of the impeller of the pump 904 according tothree-phase alternating current (AC) electrical power provided from thecontrol system via power cables 950 and a junction box 952 on thehousing of the motor 906. The power to the pump 904 may be determined bymeasuring the current and voltage provided to the motor 906 andcomputing pump power based on current, voltage, speed, and motor modelinformation such as efficiency. This may be measured and computed by apower sensor 954, which provides a signal related thereto to the controlsystem 908. Alternatively or in combination, the motor drive 960 mayprovide motor torque information to the controller 966 where pump inputpower is calculated according to the torque and possibly speedinformation. Alternatively, input current and possibly voltage may bemeasured from the power lines going from the power source 962 to themotor drive 960 using a sensor 954a. Drive efficiency and/or motorefficiency equations may be used to determine the power going into thepump 904. It will be noted that either or both of the sensors 954 and954a can be integrated into the motor drive 960.

The control system 908 also comprises a motor drive 960 providingthree-phase electric power from an AC power source 962 to the motor 906via the cables 950 in a controlled fashion (e.g., at a controlledfrequency and amplitude) in accordance with a control signal 964 from acontroller 966. The controller 966 receives the process variablemeasurement signals from the atmospheric pressure sensor 944, thesuction pressure sensor 924, the discharge pressure sensor 940, the flowsensor 938, the temperature sensor 942, the speed sensor 946, thevibration sensor 948, the power sensor 954, and other process sensors941, together with the setpoint 910, and provides the control signal 964to the motor drive 960 in order to operate the pump system 902commensurate with the setpoint 910 within specified operating limits. Inthis regard, the controller 966 may be adapted to control the system 902to maintain a desired fluid flow rate, outlet pressure, motor (pump)speed, torque, suction pressure, or other performance characteristic.

Setup information 968 may be provided to the controller 966, which mayinclude operating limits (e.g., min/max speeds, min/max flows, min/maxpump power levels, min/max pressures allowed, NPSHR values, and thelike), such as are appropriate for a given pump 904, motor 906, pipingand process conditions, and/or process dynamics and other systemconstraints. The control system 908 provides for operation within anallowable operating range about the setpoint 910, whereby the system 902is operated at a desired operating point within the allowable range, inorder to optimize one or more performance characteristics (e.g., such aslife cycle cost, efficiency, life expectancy, safety, emissions,operational cost, MTBF, noise, vibration, and the like).

Referring also to FIG. 10, the controller 966 comprises an optimizationcomponent 970, which is adapted to select the desired operating pointfor pump operation within the allowable range about the setpoint 910,according to an aspect of the invention. As illustrated and describedhereinafter, the optimization component 970 may be employed to optimizeefficiency or other performance characteristics or criteria, includingbut not limited to throughput, lifetime, or the like. The component 970,moreover, may select the desired operating point according toperformance characteristics associated with one or more components inthe system 902 or associated therewith. For example, the optimizationcomponent 970 may generate an optimization signal 972 by correlatingpump, motor, and or motor drive efficiency information associated withthe pump 904, motor 906, and motor drive 960, respectively, to derive acorrelated process efficiency associated with the entire system 902.

Such component efficiency information may be obtained, for example, fromsetup information 969 such as efficiency curves for the pump 904, motor906, and drive 960 alone or in combination with such information derivedfrom one or more of the sensors 924, 938, 940, 941, 942, 944, 946, 954,954a, and/or 948. In this manner, the efficiency of a particularcomponent (e.g., pump 904, motor 906, and drive 960) in the system 902may be determined from manufacturer data, which may be supplemented,enhanced, or replaced with actual measured or computed efficiencyinformation based on prior operation and/or diagnosis of one or moresuch components.

The optimization component 970, moreover, may correlate efficiencyinformation related to the components of the system 902, along with suchefficiency information related to components of a larger process orsystem of which the system 902 is a part, in order to select the desiredoperating point for optimization of overall system efficiency. Thus, forexample, the controller 966 may generate the control signal 964 to themotor drive 960 according to the optimization signal 972 from theoptimization component 970, based on the optimum efficiency point withinthe allowable operating range according to the correlated processefficiency for the system 902. Furthermore, it will be appreciated thatperformance information associated with components in unrelated systemsmay be employed (e.g., efficiency information related to motors inother, unrelated systems within a manufacturing facility) in optimizingenergy usage across the entire facility.

Alternatively or in combination, the controller 966 may operate the pumpwithin the allowable range about the setpoint 910 in order to achieveglobal optimization of one or more performance characteristics of alarger process or system of which the pump system 902 is a part. Thus,for example, the components (e.g., pump 904, motor 906, drive 960) ofthe system 902 may be operated at less than optimal efficiency in orderto allow or facilitate operation of such a larger process at optimalefficiency. The controller 966 selectively provides the control signal964 to the motor drive 960 according to the setpoint 910 (e.g., in orderto maintain or regulate a desired flow rate) as well as to optimize aperformance characteristic associated with the system 902 or a largerprocess, via the optimization signal 972. Thus, in one example flowcontrol is how optimization is achieved in this example. It will benoted that the allowable range of operation may be provided in lieu ofan actual setpoint, or the allowable range may be derived using thesetpoint value 910.

In this regard, the controller 966 may provide the control signal 964 asa motor speed signal 964 from a PID control component 974, which inputsprocess values from one or more of the sensors 924, 938, 940, 942, 944,946, 948, 954, and 954a, economic values 916, and the setpoint 910,wherein the magnitude of change in the control signal 964 may be relatedto the degree of correction required to accommodate the present controlstrategy, for example, such as system efficiency, and/or the error inrequired versus measured process variable (e.g., flow). Although theexemplary controller 966 is illustrated and described herein ascomprising a PID control component 974, control systems and controllersimplementing other types of control strategies or algorithms (e.g., PIcontrol, PID with additional compensating blocks or elements,stochastics, non-linear control, state-space control, model reference,adaptive control, self-tuning, sliding mode, neural networks, GA, fuzzylogic, operations research (OR), linear programming (LP), dynamicprogramming (DP), steepest descent, or the like) are also contemplatedas falling within the scope of the present invention.

The exemplary PID component 974 may compare a measured process variable(e.g., flow rate measured by sensor 938) with the desired operatingpoint within the allowable range about the setpoint 910, where thesetpoint 910 is a target setpoint flow rate, and wherein one or more ofthe process variable(s) and/or the desired operating point (e.g., aswell as the allowable operating range about the setpoint) may be scaledaccordingly, in order to determine an error value (not shown). The errorvalue may then be used to generate the motor speed signal 964, whereinthe signal 964 may vary proportionally according to the error value,and/or the derivative of the error, and/or the integral of the error,according to known PID control methods.

The controller 966 may comprise hardware and/or software (not shown) inorder to accomplish control of the process 902. For example, thecontroller 966 may comprise a microprocessor (not shown) executingprogram instructions for implementing PID control (e.g., PID component974), implementing the efficiency or other performance characteristicoptimization component 970, inputting of values from the sensor signals,providing the control signal 964 to the motor drive 960, and interactingwith the user interface 911, the network 914, modem 917, and thetransceiver 915. The user interface 911 may allow a user to inputsetpoint 910, setup information 968, and other information, and inaddition may render status and other information to the user, such assystem conditions, operating mode, diagnostic information, and the like,as well as permitting the user to start and stop the system and overrideprevious operating limits and controls. The controller 966 may furtherinclude signal conditioning circuitry for conditioning the processvariable signals from the sensors 916, 924, 938, 940, 941, 942, 944,946, 948, and/or 954.

The controller 966, moreover, may be integral with or separate from themotor drive 960. For example, the controller 966 may comprise anembedded processor circuit board mounted in a common enclosure (notshown) with the motor drive 960, wherein sensor signals from the sensors916, 924, 938, 940, 941, 942, 944, 946, 948, and/or 954 are fed into theenclosure, together with electrical power lines, interfaces to thenetwork 914, connections for the modem 917, and the transceiver 915, andwherein the setpoint 910 may be obtained from the user interface 911mounted on the enclosure, and/or via a network, wireless, or Internetconnection. Alternatively, the controller 966 may reside as instructionsin the memory of the motor drive 960, which may be computed on anembedded processor circuit that controls the motor 906 in the motordrive 960.

In addition, it will be appreciated that the motor drive 960 may furtherinclude control and feedback components (not shown), whereby a desiredmotor speed (e.g., as indicated by the motor speed control signal 964from the PID component 974) is achieved and regulated via provision ofappropriate electrical power (e.g., amplitude, frequency, phasing, etc.)from the source 962 to the motor 906, regardless of load fluctuations,and/or other process disturbances or noise. In this regard, the motordrive 960 may also obtain motor speed feedback information, such as fromthe speed sensor 946 via appropriate signal connections (not shown) inorder to provide closed loop speed control according to the motor speedcontrol signal 964 from the controller 966. In addition, it will beappreciated that the motor drive 960 may obtain motor speed feedbackinformation by means other than the sensor 946, such as throughinternally computed speed values, as well as torque feedbackinformation, and that such speed feedback information may be provided tothe controller 966, whereby the sensor 946 need not be included in thesystem 902. One control technique where the motor drive 960 may obtaintorque and speed information without sensors is when running in avector-control mode.

As further illustrated in FIG. 11, the optimization component 970correlates component performance information (e.g., efficiencyinformation) associated with one or more components (e.g., pump 704,motor 706, motor drive 760, etc.) in the system 702 in order to derivecorrelated process performance information. In addition, the component970 may employ performance information associated with other componentsin a larger process (not shown) of which the system 702 is a part, inorder to derive correlated performance information. It will beappreciated that the optimization component 970, moreover, may correlateinformation other than (or in addition to) efficiency information,including but not limited to life cycle cost, efficiency, lifeexpectancy, safety, emissions, operational cost, MTBF, noise, vibration,and the like.

The optimization component 970 selects the desired operating point asthe optimum performance point within the allowable range of operationaccording to the correlated process performance information. Asillustrated in FIG. 9, the controller 966 may obtain pump efficiencyinformation 900 related to the pump 704, motor efficiency information902 related to the motor 706, and motor drive efficiency information 904related to the motor drive 760, which is provided to a correlationengine 910 in the optimization component 970. The correlation engine 910correlates the information 900, 902, and/or 904 according to presentoperating conditions (e.g., as determined according to values from oneor more of the process sensors 924, 938, 940, 941, 942, 944, 946, 948,and/or 954, economic value(s) 916, setpoint 910, and allowable operatingrange information from setup information 968) in order to determine adesired operating point within the allowable operating range at whichthe efficiency of the system 902 or a larger process (not shown) may beoptimal.

In this regard, the correlation engine 1110 may compute, predict, orderive correlated system efficiency information 1112 from thecorrelation of one or more of the pump efficiency information 1100related to the pump 1104, motor efficiency information 1102 related tothe motor 906, and motor drive efficiency information 904 related to themotor drive 960. The correlation may be accomplished in the correlationengine 1110 through appropriate mathematical operations, for example, insoftware executing on a microprocessor within the controller 966.Appropriate weighting factors may be assigned to the relevantinformation being correlated (e.g., 1100, 1102, and 1104), for instance,whereby the efficiency of the pump 904 may be given more weight thanthat of the motor drive 960. The invention can also be employed toprovide near-optimal operation to enhance robustness (e.g., to reducesensitivity), in order to provide better overall optimization.

The correlation engine 1110, moreover, may determine correlated systemefficiency information according to the current operating conditions ofthe system 902, such as the process setpoint 910, diagnosed degradationof system components, etc. Thus, for example, the correlated systemefficiency information 1112 may include different desired operatingpoints depending on the setpoint 910, and/or according to the currentpressures, flow rates, temperatures, vibration, power usage, etc., inthe system 902, as determined by the values from one or more of thesensors 924, 938, 940, 941, 942, 944, 946, 948, and/or 954. Thecontroller 966 then provides the control signal 964 as a motor speedsignal 964 to the motor drive 960 according to the desired operatingpoint. In addition to efficiency information (e.g., 1100, 1102, 1104)the component performance information may also comprise one or more oflife cycle cost information, efficiency information, life expectancyinformation, safety information, emissions information, operational costinformation, MTBF information, noise information, and vibrationinformation. The correlation engine 1110 can also comprise algorithmsemploying temporal logic. This permits the correlation engine 1110 toestablish dynamic, time varying control signals to optimize systemoperation over a time horizon. For example, if energy costs are to riseduring peak daytime periods, the correlation engine may prescribe aslightly higher throughput during off-peak hours (e.g., less energyefficient during off-peak hours) in order to minimize operation duringmore costly peak energy cost periods.

FIGS. 12-14 illustrate examples of component performance characteristicinformation, which may be correlated (e.g., via the correlation engine1110) in order to select the desired operating point for the system 902.FIG. 12 illustrates a plot of an exemplary pump efficiency curve 1200(e.g., related to pump 904), plotted as efficiency 1210 (e.g., outputpower/input power) versus pump speed 1220. The exemplary curve 1200comprises a best operating point 1230, whereat the pump efficiency isoptimal at approximately 62% of maximum rated pump speed. The pumpefficiency information 1100 of the optimization component 970 maycomprise one or more such curves, for example, wherein different curvesexist for different flow rates, pressures, temperatures, viscosity ofpumped fluid, etc. Similarly, FIG. 13 illustrates a plot of an exemplarymotor efficiency curve 1300 (e.g., related to motor 906), plotted asefficiency 1310 (e.g., output power/input power) versus motor speed1320. The exemplary curve 1300 comprises a best operating point 1330,whereat the motor efficiency is optimal at approximately 77% of maximumrated speed.

It will be appreciated from the curves 1200 and 1300 of FIGS. 12 and 13,respectively, that the optimal efficiency operating points forindividual components (e.g., pump 904 and motor 906) of the system 902,or of typical motorized systems generally, may not, and seldom do,coincide. The pump efficiency information 1100 of the optimizationcomponent 970 may comprise one or more such curves 1230 of pumpefficiency versus speed, for example, wherein a different curve existsfor different flow rates, pressures, viscosity of pumped fluid, motorload, etc. In like fashion, FIG. 14 illustrates a plot of an exemplarymotor drive efficiency curve 1400 (e.g., related to the motor drive 960of system 902), plotted as efficiency 1410 (e.g., output power/inputpower) versus motor (e.g., pump) speed 1420. The exemplary curve 1400comprises a best operating point 1430, whereat the motor driveefficiency is optimal at approximately 70% of the rated speed. The motordrive efficiency information 1104 of the optimization component 970 maycomprise one or more such curves, for example, wherein a different curveexists for different flow rates, temperatures, torques, pressures,viscosity of pumped fluid, motor load, motor temperature, etc.

The correlation engine 1110 of the efficiency optimization component 970correlates the three curves 1200, 1300, and 1400 in order to derivecorrelated system efficiency information 1112. Referring now to FIG. 15,the correlation engine may correlate the curves 1200, 1300, and 1400 toderive a correlated system efficiency curve 1500 plotted as systemefficiency optimization 1510 versus speed 1520. The exemplary curve 1500comprises a peak optimization point 1530 at approximately 71% of ratedspeed. This composite performance characteristic curve 1500 may then beemployed by the optimization component 970 in order to select thedesired operation point for the system 902, which may be provided to thePID 974 via the optimization signal 972.

As illustrated in FIG. 15, where the allowable operating range includesan upper limit 1540, and a lower limit 1550 (e.g., where these limits1540 and 1550 are scaled from process units, such as flow in GPM intospeed), the optimization component 970 may advantageously select thepeak optimization point 1530 at approximately 71% of rated speed, inorder to optimize the efficiency within the allowable operating range.In another example, where the allowable upper and lower limits 1560 and1570 are specified, a local optimum 1580 within that range may beselected as the desired operating point. Many other forms of performanceinformation and correlations thereof are possible within the scope ofthe present invention, beyond those illustrated and described above withrespect to FIGS. 12-15. The preceding discussion described sending amotor speed signal (e.g., signal 964) to the motor drive 960.Alternatively or in combination, other drive parameters (e.g., carrierfrequency, control mode, gains, and the like) can be changed, enhanced,modified, etc., in accordance with the invention. This can enable evenmore efficient operation, for example, by changing the efficiency 1500.

Referring now to FIGS. 16-20, the optimization aspects of the inventionmay be employed across a plurality of controllers operating variousactuators (e.g., valves, switches, and the like) and motorized systems(e.g., pumps, mixers, compressors, conveyors, fans, and the like) in alarge process or system 1600, for optimization of one or moreperformance characteristics for unrelated motorized systems. Suchsub-systems may comprise individual controllers, such as valvecontrollers, motor controllers, as well as associated motors and drives.As illustrated in FIG. 16, an integer number N of such individual motorcontrollers MC1 through MCN may be networked together via a network1602, allowing peer-to-peer communication therebetween, wherein MC1controls a motorized pump PUMP1 via a motor M1 and associated motordrive MD1, and MCN controls a motorized pump PUMPN via a motor MN andassociated motor drive MDN. Other controllers, such as valve controllerVC1 may be connected to the network 1602, and operative to control avalve VALVE1. It is to be appreciated that the motor controller may beembedded in the motor drive such that MC1 and MD1 are one component.

The controllers MC1-MCN and VC1 may exchange information relating toprocess conditions (e.g., flow, pressure, power, efficiency, temperature. . . ), control information (e.g., setpoints, control outputs, alarmconditions, process limits . . . ), and performance characteristicinformation (e.g., related to life cycle cost information, efficiencyinformation, life expectancy information, safety information, emissionsinformation, operational cost information, MTBF information, noiseinformation, vibration information, production requirements, deliveryschedules, and the like). One or more of the individual controllers MC1,MCN, and VC1 may determine desired operating points for the associatedsub-systems according to performance characteristic information obtainedfrom other controllers via the network 1602, and/or from sensorsassociated with the individual sub-systems.

Another possible configuration is illustrated in FIG. 17, wherein a hostcomputer 1704 is connected to the network 1702. The host 1704 mayprovide centralized operation of the pumps PUMP1 and PUMPN as well as ofthe valve VALVE1, for example, by providing setpoint information to theassociated controllers MC1, MCN, and VC1. Other information may beexchanged between the computer 1704 and the various controllers MC1,MCN, and VC1 in host-to-peer fashion, such as information relating toprocess conditions, control information, and performance characteristicinformation, whereby an efficiency optimization component 1706 in thehost computer 1704 may determine desired operating points for one ormore of the controllers MC1, MCN, and VC1 according to one or moreperformance characteristics associated with the system 1700.Alternatively or in combination, one or more of the individualcontrollers MC1,

MCN, and VC1 may determine desired operating points for the associatedsub-systems according to performance characteristic information obtainedfrom the host computer 1704, from other controllers via the network1702, and/or from sensors associated with the individual sub-systems.

Referring now to FIG. 18, another process 1500 is illustrated forproviding material from first and second tanks TANK1 and TANK2 into amixing tank TANK3 via pumps PUMP1 and PUMP2 with associated motors,drives and controllers. The material is mixed in TANK3 via a motorizedmixer with associated motor M3, drive MD3, and controller MC3. Mixedmaterial is then provided via a motorized pump PUMP3 and control valveVALVE1 to a molding machine 1502 with an associated motor M5, whereaftermolded parts exit the machine 1502 via a chute 1504 to a motorizedconveyor 1506 controlled by motor M6, which transports the molded partsto a cooler device 1508 having a motorized compressor 1510. The cooledparts are then provided to a second motorized conveyor 1512 whereat amotorized fan facilitates removal of moisture from the parts.

The various motor and valve controllers MC1-MC9 and VC1 associated withthe various sub-systems of the process 1500 are networked together via anetwork 1520 in order to provide peer-to-peer or other types ofcommunications therebetween. One or more of these controllers MC1-MC9and VC1 may be adapted to correlate performance characteristicinformation associated with component devices (e.g., motors, drives,valves) in order to determine desired operating points for one, some, orall of the sub-systems in the process 1500 in accordance with theinvention.

A host computer 1532, moreover, may be provided on the network 1520,which may comprise an optimization component 1532 operative to determinedesired operating points (e.g., as well as setpoints, allowableoperating ranges about such setpoints, and the like) for one or more ofthe sub-systems in the process 1500 according to one or more performancecharacteristics associated with the process 1500, which may be thencommunicated to the various controllers MC1-MC9 and VC1 in order tooptimize performance of the process 1500 in some aspect (e.g.,efficiency, cost, life cycle cost, throughput, efficiency, lifeexpectancy, safety, emissions, operational cost, MTBF, noise, vibration,and the like). Thus, in accordance with the present invention, theprocess 1500 may be operated to both produce molded parts from rawmaterials, and at the same time to optimize one or more performancemetrics, such as cost per part produced. Operation of the system may becontrolled such that prognostic information regarding machinery failure,expected delivery of repair parts, and expected energy costs isconsidered in defining an optimum operating mode. For example, if themolding machine is predicted to fail in one week, then increasedwork-in-process inventory may be generated while the needed repair partsare automatically ordered and delivery expedited. Alternatively a moreoptimum control mode may be to operate the molding machine very slow andslow down other process equipment to maintain a lower production ratebut a continuous flow of finished products.

Another aspect of the invention provides a methodology by which amotorized system may be controlled. The methodology comprises selectinga desired operating point within an allowable range of operation about asystem setpoint according to performance characteristics associated withone or more components in the system, and controlling the systemaccording to the desired operating point. The selection of the desiredoperating point may include correlating component performanceinformation associated with one or more components in the system inorder to derive correlated system performance information, and selectingthe desired operating point as the optimum performance point within theallowable range of operation according to the correlated systemperformance information. The performance information, setpoint, and/orthe allowable operating range may be obtained from a user or anotherdevice via a user interface, via a host computer or other controllerthrough a network, via wireless communications, Internet, and/oraccording to prior operation of the system, such as through trendanalysis.

An exemplary method 1900 is illustrated in FIG. 19 for controlling amotorized system in accordance with this aspect of the invention. Whilethe exemplary method 1900 is illustrated and described herein as aseries of blocks representative of various events and/or acts, thepresent invention is not limited by the illustrated ordering of suchblocks. For instance, some acts or events may occur in different orderand/or concurrently with other acts or events, apart from the orderingillustrated herein, in accordance with the invention. Moreover, not allillustrated blocks, events, or acts, may be required to implement amethodology in accordance with the present invention. In addition, itwill be appreciated that the exemplary method 1900, as well as othermethods according to the invention, may be implemented in associationwith the pumps and systems illustrated and described herein, as well asin association with other motorized systems and apparatus notillustrated or described, including but not limited to fans, conveyorsystems, compressors, gear boxes, motion control devices, screw pumps,mixers, as well as hydraulic and pneumatic machines driven by motors orturbo generators.

Beginning at 1902, the method 1900 comprises obtaining a system setpointat 1904, and obtaining an allowable operating range at 1906. Thesetpoint and operating range may be obtained at 1904 and 1906 from auser or a device such as a controller, a host computer, or the like, viaa user interface, a network, an Internet connection, and/or via wirelesscommunication. At 1908, component performance information is obtained,which may be related to components in the system and/or components in alarger process of which the controlled system is a part. Componentperformance information may be obtained from vendor data, frome-commerce web sites, from measured historical data, or from simulationand modeling or any combination of this these. The component performanceinformation is then correlated at 1910 in order to derive correlatedsystem performance information. At 1912, a desired operating point isselected in the allowable operating range, according to the correlatedsystem performance information derived at 1910. The system is thencontrolled at 1914 according to the desired operating point, whereafterthe method 1900 returns to 1908 as described above. Process changes,disturbances, updated prognostic information, revised energy costs, andother information may require periodic evaluation and appropriatecontrol adjustment in order to ensure meeting optimum performance levelsas the process changes (e.g., tanks empty, temperature changes, or thelike) and optimizing asset utilization.

Another aspect of the invention provides for controlling a motorizedsystem, such as a pump, wherein a controller operatively associated withthe system includes a diagnostic component to diagnose an operatingcondition associated with the pump. The operating conditions detected bythe diagnostic component may include motor, motor drive, or pump faults,pump cavitation, pipe breakage or blockage, broken impeller blades,failing bearings, failure and/or degradation in one or more systemcomponents, sensors, or incoming power, and the like. The controllerprovides a control signal to the system motor drive according to asetpoint and a diagnostic signal from the diagnostic component accordingto the diagnosed operating condition in the pump. The diagnosticcomponent may perform signature analysis of signals from one or moresensors associated with the pump or motorized system, in order todiagnose the operating condition. Thus, for example, signal processingmay be performed in order to ascertain wear, failure, or otherdeleterious effects on system performance, whereby the control of thesystem may be modified in order to prevent further degradation, extendthe remaining service life of one or more system components, or toprevent unnecessary stress to other system components. In this regard,the diagnostic component may process signals related to flow, pressure,current, noise, vibration, temperature, and/or other parameters ofmetrics associated with the motorized system. Such a system will be ableto effectively control the remaining useful life of the motorizedsystem.

Referring now to FIG. 20, another exemplary pump system 2002 isillustrated, in which one or more aspects of the invention may becarried out. The system 2002 includes a pump 2004, a three phaseelectric motor 2006, and a control system 2008 for operating the system2002 in accordance with a setpoint 2010. While the exemplary motor 2006is illustrated and described herein as a polyphase synchronous electricmotor, the various aspects of the present invention may be employed inassociation with single-phase motors as well as with DC and other typesof motors. In addition, the pump 2004 may comprise a centrifugal typepump, however, the invention finds application in association with otherpump types not illustrated herein, for example, positive displacementpumps. Additionally other motor-driven equipment such as centrifugalcompressors, reciprocating compressors, fans, motor-operated valves andother motor driven equipment can be operated with a controller in adynamic environment.

The control system 2008 operates the pump 2004 via the motor 2006according to the setpoint 2010 and one or more measured processvariables, in order to maintain operation of the system 2002commensurate with the setpoint 2010 and within the allowable processoperating ranges specified in setup information 2068, supplied to thecontrol system 2008 via a user interface 2011. For example, it may bedesired to provide a constant fluid flow, wherein the value of thesetpoint 2010 is a desired flow rate in gallons per minute (GPM) orother engineering units. The setup information 2068, moreover, maycomprise an allowable range of operation about the setpoint 2010 (e.g.,expressed in GPM, percentage of process variable span, or other units),wherein the control system 2008 may operate the system 2002 at anoperating point within the allowable range.

Alternatively or in combination, setup information, setpoints, and otherinformation may be provided to the control system 2008 by a user 2012via a host computer 2013 operatively connected to a network 2014, and/orby wireless communications via a transceiver 2015. Such information maybe provided via the network 2014 and/or the wireless communicationstransceiver 2015 from a host computer (e.g., computer 2013) and/or fromother controllers (e.g., PLCs, not shown) in a larger process, whereinthe setpoint 2010, and/or setup information are provided to the controlsystem 2008, as illustrated and described in greater detail hereinafter.The control system 2008, moreover, may include a modem 2017 allowingcommunication with other devices and/or users via a globalcommunications network, such as the Internet 2018.

The pump 2004 comprises an inlet opening 2020 through which fluid isprovided to the pump 2004 in the direction of arrow 2022 as well as asuction pressure sensor 2024, which senses the inlet or suction pressureat the inlet 2020 and provides a corresponding suction pressure signalto the control system 2008. Fluid is provided from the inlet 2020 to animpeller housing 2026 including an impeller (not shown), which rotatestogether with a rotary pump shaft coupled to the motor 2006 via acoupling 2028. The impeller housing 2026 and the motor 2006 are mountedin a fixed relationship with respect to one another via a pump mount2030, and motor mounts 2032. The impeller with appropriate fin geometryrotates within the housing 2026 so as to create a pressure differentialbetween the inlet 2020 and an outlet 2034 of the pump 2004. This causesfluid from the inlet 2020 to flow out of the pump 2004 via the outlet ordischarge tube 2034 in the direction of arrow 2036. The flow rate offluid through the outlet 2034 is measured by a flow sensor 2038, whichprovides a flow rate signal to the control system 2008.

In addition, the discharge or outlet pressure is measured by a pressuresensor 2040, which is operatively associated with the outlet 2034 andprovides a discharge pressure signal to the control system 2008. It willbe noted at this point that although one or more sensors (e.g., suctionpressure sensor 2024, discharge pressure sensor 2040, outlet flow sensor2038, and others) are illustrated in the exemplary system 2002 as beingassociated with and/or proximate to the pump 2004, that such sensors maybe located remote from the pump 2004, and may be associated with othercomponents in a process or system (not shown) in which the pump system2002 is employed. In this regard, other process sensors 2041 may beconnected so as to provide signals to the control system 2008, forexample, to indicate upstream or downstream pressures, flows,temperatures, levels, or the like. Alternatively, flow may beapproximated rather than measured by utilizing differential pressureinformation, pump speed, fluid properties, and pump geometry informationor a pump model (e.g., CFD model). Alternatively or in combination,inlet and/or discharge pressure values may be estimated according toother sensor signals (e.g., 2041) and pump/process information.

In addition, it will be appreciated that while the motor drive 2060 isillustrated in the control system 2008 as separate from the motor 2006and from the controller 2066, that some or all of these components maybe integrated. Thus, for example, an integrated, intelligent motor maybe provided with the motor 2006, the motor drive 2060 and the controller2066. Furthermore, the motor 2006 and the pump 2004 may be integratedinto a single unit (e.g., having a common shaft wherein no coupling 2028is required), with or without integral control system (e.g., controlsystem 2008, comprising the motor drive 2060 and the controller 2066) inaccordance with the invention.

The control system 2008 further receives process variable measurementsignals relating to pump temperature via a temperature sensor 2042,atmospheric pressure via a pressure sensor 2044 located proximate thepump 2004, motor (pump) rotational speed via a speed sensor 2046, andvibration via sensor 2048. The motor 2006 provides rotation of theimpeller of the pump 2004 according to three-phase alternating current(AC) electrical power provided from the control system via power cables2050 and a junction box 2052 on the housing of the motor 2006. The powerto the pump 2004 may be determined by measuring the current provided tothe motor 2006 and computing pump power based on current, speed, andmotor model information. This may be measured and computed by a powersensor 2054 or 2054A, which provides a signal related thereto to thecontrol system 2008. Alternatively or in combination, the motor drive2060 may provide motor torque information to the controller 2066 wherepump input power is calculated according to the torque and possiblyspeed information and motor model information.

The control system 2008 also comprises a motor drive 2060 providingthree-phase electric power from an AC power source 2062 to the motor2006 via the cables 2050 in a controlled fashion (e.g., at a controlledfrequency and amplitude) in accordance with a control signal 2064 from acontroller 2066. The controller 2066 receives the process variablemeasurement signals from the atmospheric pressure sensor 2044 (2054a),the suction pressure sensor 2024, the discharge pressure sensor 2040,the flow sensor 2038, the temperature sensor 2042, the speed sensor2046, the vibration sensor 2048, the power sensor 2054, and otherprocess sensors 2041, together with the setpoint 2010, and provides thecontrol signal 2064 to the motor drive 2060 in order to operate the pumpsystem 2002 commensurate with the setpoint 2010. In this regard, thecontroller 2066 may be adapted to control the system 2002 to maintain adesired fluid flow rate, outlet pressure, motor (pump) speed, torque,suction pressure, tank level, or other performance characteristic.

Setup information 2068 may be provided to the controller 2066, which mayinclude operating limits (e.g., min/max speeds, min/max flows, min/maxpump power levels, min/max pressures allowed, NPSHR values, and thelike), such as are appropriate for a given pump 2004, motor 2006, andpiping and process conditions. The controller 2066 comprises adiagnostic component 2070, which is adapted to diagnose one or moreoperating conditions associated with the pump 2004, the motor 2006, themotor drive 2060, and/or other components of system 2002. In particularthe controller 2066 may employ the diagnostic component 2070 to providethe control signal 2064 to the motor drive 2060 according to setpoint2010 and a diagnostic signal (not shown) from the diagnostic component2070 according to the diagnosed operating condition in the pump 2004 orsystem 2002. In this regard, the diagnosed operating condition maycomprise motor or pump faults, pump cavitation, or failure and/ordegradation in one or more system components. The controller 2066 mayfurther comprise an optimization component 2070a, operating in similarfashion to the optimization component 70 illustrated and describedabove.

The diagnostic component may advantageously perform signature analysisof one or more sensor signals from the sensors 2024, 2038, 2040, 2041,2042, 2044, 2046, 2048, and/or 2054, associated with the pump 2004and/or the system 2002 generally, in order to diagnose one or moreoperating conditions associated therewith. Such signature analysis maythus be performed with respect to power, torque, speed, flow, pressure,and other measured parameters in the system 2004 of in a larger processof which the system 2002 is a part. In addition, the signature analysismay comprise frequency analysis employing Fourier transforms, spectralanalysis, space vector amplitude and angular fluctuation, neuralnetworks, data fusion techniques, model-based techniques, discreteFourier transforms (DFT), Gabor transforms, Wigner-Ville distributions,wavelet decomposition, non-linear filtering based statisticaltechniques, analysis of time series data using non-linear signalprocessing tools such as Poincare' maps and Lyapunov spectrumtechniques, and other mathematical, statistical, and/or analyticaltechniques. The diagnostic features of the component 2070, moreover, maybe implemented in hardware, software, and/or combinations thereof in thecontroller 2066.

Such techniques may be used to predict the future state or health ofcomponents in the system 2002 (e.g., and/or those of a larger system ofwhich system 2002 is a part or with which system 2002 is associated).This prognostics will enable the control to be altered to redistributestress, to control the time to failure, and/or the remaining useful lifeof one or more such components or elements. It will be appreciated thatsuch techniques may be employed in a larger system, such as the system300 of FIG. 10, for example, wherein a known or believed good componentor sub-system may be overstressed to allow another suspected weakenedcomponent to last longer.

FIG. 21 provides further illustration 2100 of enterprise resourceplanning (ERP) component 184 that, in accordance with aspects of theclaimed subject matter, can facilitate and/or effectuate utilization ofpredictive enterprise manufacturing intelligence (EMI) facilities inorder to provide the ability to conceptualize and display current,scheduled, forecasted, potentially possible, hypothetical, and/orpredicted process conditions. As illustrated, enterprise resourceplanning component 184 can include capacity management component 2102,energy optimization component 2104, and profit optimization component2106. In relation to enterprise resource planning component 184 sincemuch, though not all, of the configuration and operation of thiscomponent is substantially similar to that described in relation toFIGS. 1a -1 k, and FIG. 1k in particular, a detailed description of suchfeatures, unless where necessary, has been omitted for the sake ofbrevity and to avoid needless prolixity.

Capacity management component 2102 can leverage process models tovisually present real-time, dynamic comparisons of a process'theoretical capacity and its current production rate. Capacitymanagement component 2102 can provide timely visibility into potentialcapacity from existing factors of production (e.g., resources employedto produce goods and/or services) thereby avoiding latency of decisions.Capacity management component 2102 can perform dynamic constraintprofiling based at least in part on current and/or predicted operatingconditions, and by linking into a corporate business system, canautomatically quantify the potential gains of increased capacity as aresult of driving production up to prevailing constraints. The potentialgains can be further characterized as a probability or likelihoodmeasure of potential economic gain.

Additionally, capacity management component 2102 can contain or utilizea built-in framework for instantaneous analysis of potential scenariosto achieve optimal capacity by product, shift, and/or diverse anddisparate production site. This functionality can allow plant facilitymanagement the ability to analyze tradeoffs associated with the multiplechoices available to achieve optimal production, resulting in faster andmore accurate and timely capture of business opportunities from improveddecision making.

As those reasonably cognizant in this field of endeavor will no doubt beaware, today production analysis is typically based on historical dataand user-defined spreadsheets. In some cases, data mining tools can beemployed in conjunction with real-time or near-real time data fromcontrol infrastructure, yet this technique is inherently retrospectiveand its value is limited to understanding what happened. In contrast,capacity management component 2102, in conjunction with various aspectsof enterprise resource planning component 184, leverages predictivetechnologies and integrates financial variables with high fidelitymodels that can be utilized to control processes, to provide users theability to understand the economic value of opportunities as theseunfold, and the ability to capture profitable opportunities or shednon-profitable opportunities proactively and with a greater degree ofconfidence.

Moreover, as those of reasonable skill in this field of endeavor will beequally aware, production facilities can make significant investments incapital improvement projects, aiming to streamline production andidentifying and resolving bottlenecks in manufacturing units usinganecdotal evidence based at least in part on a plant or productionfacility's historical performance. Often, for example, a major capitalasset is replaced with the expectation that the removal of thisprevailing constraint will result in production improvements, only tolearn that the achieved improvement is of minimal or marginal benefitbecause the available capacity to the next constraint is miniscule.Capacity management component 2102, in concert with and throughutilization of the disparate and various capabilities associated withenterprise resource planning component 184, can automatically determineor identify a facility's top constraints (e.g., top 5, 10, 20, . . . ,constraints) and quantifies the latent capacity available across theseidentified constraints, providing operations management with financialprofiles of production opportunities restricted by these constraints.Capacity management component 2102 can thus allow for capitalexpenditure planning with a greater degree of confidence, having athorough understanding of the potential economic improvements associatedwith de-bottlenecking projects.

Energy optimization component 2104 in order to present visualizations ofeconomic optima that meet a plant or production facility's predictedenergy demand can, together with modeling frameworks and disparatepredictive capabilities, utilize multiple sub-models of production,utilities, and emissions integrated with a plant or productionfacility's (or business entities) financial system. Energy optimizationcomponent 2104 can create an integrated energy-supply model byincorporating the variable costs associated with an entities businesssystems, economic sub-models can be constructed for eachenergy-generating asset at a production facility in order to determineeach asset's financial profile, taking into account their generatingcapacity, efficiency curves, reliability, and operating costs. Each ofthese asset sub-models can be combined to create a production facility'sholistic energy-supply model.

Additionally, energy optimization component 2104 can create theproduction facility's energy-demand model by leveraging powerfuloptimization or predictive engines. From the created energy-demandmodel, sub-models of production can be developed in order to determine,at user defined time horizons, predicted energy demands based at leastin part on current and prospective operating objectives. Further, energyoptimization component 2104 can integrate the developed energy-supplyand energy-demand models to produce an energy optimization model. Theintegration of the developed energy-supply and energy-demand models canbe integrated using a modeling framework to solve economic supply optimaand expose the most cost-effective energy-generating assets available tomeet predicted demand. For enterprises that operate under greeninitiatives or corporate sustainability programs, energy optimizationcomponent 2104 can, for instance, integrate a model of each asset'semissions thereby ensuring that the economic optimum incorporates theenvironmental impact associated with meeting the production facility'senergy demand. This model can be further expanded to include aprobabilistic estimation components, sensitivity analysis components,and adaptive modeling components. The probabilistic component can, forexample, maximize the certainty of achieving a level of economic benefitor financial return on an investment. The sensitivity analysis componentcan identify factors and operating strategies that while showingexcellent results, can be brittle and can suffer from the effects ofunmodeled disturbances or events that can potentially take place. Theadaptive modeling component can continually assess the impact ofhistorical decisions and use this information to generate modelstructure or parameter changes, to establish causal relationships thatcan exist in the model, to improve the stochastic measures assigned tooutcomes, or to generate additional rules or heuristics for futureeconomic analysis and decision making functions.

It should be noted without limitation or loss of generality thatdeveloped or created models can be integrated by energy optimizationcomponent 2104 in series, parallel, nested, or in a networked structureto provide the most efficient solution to attain an economic objective.The goal of energy optimization component 2104 is to provide timelyvisibility into the most cost-effective source of energy to meet thepredicted demand from production, while ensuring full environmentalcompliance. Accordingly, energy optimization component 2104 can containbuilt-in decision support frameworks for instantaneous analysis ofpotential scenarios for decision support. Production facilities withavailable third party sources of energy can thus incorporate thefinancial parameters (e.g., scheduling production runs during lower costoff peak energy windows, etc.) of their supply contracts to support makevs. buy decisions based at least in part on the production facilitiespredicted demand. The system can generate a set of potential scenariosand establish their potential benefit. The system can operate in agenerative mode and sequentially establish new operating scenarios in amanner that progressively provide increased economic value and return onthe investment. Various search and optimization methods such as thegradient search method previously presented can be used. Further, theexpected supply, demand, and economic value can be interpreted in thecontext of a stochastic system. Likelihood estimates can be made basedat least in part on historical data or other statistical modelingschemes.

The value of utilizing energy optimization component 2104, previouslydescribed, is to meet a production facility's energy demand at thelowest possible cost while achieving production objectives and balancingenvironmental emissions. As will be appreciated, the high cost of energyhas become the number one concern to manufactures across the globe, withno signs of abatement. Understanding the impact of energy usage atproduction facilities dispersed around the world must necessarily gobeyond anecdotal analysis of past performance, and real-time consumptionmonitoring generally only allows for reactive decision making to curtailthe cost of energy. Additionally, manufacturers often find themselvesrushing to meet energy demands from production by sourcing energywithout full knowledge of the economic impact to the organization'sprofitability. The environmental effects caused by surges in energyproduction are also typically known after the fact, risking emissionsviolations and possibly tarnishing the organization's corporate imagewith local communities, while the true cost to operations is only knownonce the financial books close well after the end of the fiscal month.

Through utilization of energy optimization component 2104, and inparticular, by leveraging the predictive capabilities of energyoptimization component 2104 and integrating financial variables into amodeling framework, energy optimization component 2104 can providemanufacturers with the ability to understand the economic balancebetween the energy demand necessary to meet production objectives andtheir production facility's energy supply capability, ensuring a greaterdegree of confidence in their decisions.

Moreover, by simultaneously profiling the different energy scenariosthat can be present by energy optimization component 2104 manufacturersand more particularly production facility managers can proactivelydetermine the most cost-effective asset configurations in order toachieve their production facility's energy demand while achievingproduction targets and still keeping environmental emissions in check.For instance, energy optimization component 2104 can be employed incampus energy management where visualizations of how many people will bein particular buildings, weather forecasting, etc., can provide richinsights into what future energy consumption will look like. Moreover,models that are developed by, or for, energy optimization component2104, or for that matter, models constructed by, or for, other aspectsof the claimed matter (e.g., capacity management component 2102 orprofit optimization component 2106) can be utilized interchangeably byany other component aspect of the claimed matter, and further aredynamic in nature. The energy optimization component 2104 can beaugmented with a scenario search component that can generate a series ofpossible operating scenarios. The resultant likely economic impact andprobability of achieving this economic impact can be evaluated.Scenarios can be progressively chosen to exploit or pursue a strategythat provides a more global optimum. In addition to the expectedeconomic benefit, also associated with each scenario is the time andcost required to realize the target scenario and the stability orbrittleness of the scenario. For example, a scenario with high economicbenefit may be difficult to sustain due to external disturbances or maypreclude transitioning to a more optimum scenario with out additionalcost, delay, or downtime. Alternatively, the scenario search method canuncover an unlikely scenario that meets all the energy and productionconstraints in an optimum manner. Such a strategy can involve operatingthe system in a unique manner that would have not been discovered bytraditional production planning methods.

Profit optimization component 2106 can utilize data and informationsupplied by capacity management component 2102 and/or energyoptimization component 2104 as well as data and information from amultiplicity of disparate other sources such as financial variables,quality components, supplier data, historical performance data, and thelike. Profit optimization component 2106, based at least in part on thesupplied data and information, can thereafter perform marginoptimization. For instance, profit optimization component 2106, wherethe process involves fabricating product X, can employ informationrelated to contracts and product schedules to analyze variable costs(e.g., energy, additives, feedstock costs, . . . ) in order to optimizeprofitability. It should be noted that profit optimization component2106 can utilize financial information in a dynamic manner rather thanin a static manner, and further can factor inefficiencies of equipment,equipment life-cycle, down-time, repair, retooling, labor cost, and thelike. Profit optimization component 2106, like capacity managementcomponent 2102 and energy optimization component 2104, can leveragepredictive technologies to optimize profits. Moreover, profitoptimization component 2106 can also employ look ahead key performanceindicators (KPIs) associated with a process or an enterprise's month-endor year-end goals to maximize profits. Additionally, profit optimizationcomponent 2106 can analyze historical opportunity costs as well asprofit velocity (e.g., how fast a certain profit can be made and howsoon it can be made) in order to learn how to drive future decisionmaking. Furthermore, profit optimization component 2106 can also includea currency arbitrage feature that can be utilized to optimizeprofitability. In exercising this currency arbitrage feature, profitoptimization component 2106 can consider the costs of goods and/orservices available based at least in part on different world currencies,locations of availability, shipment costs, production scenarios, and thelike. Furthermore, profit optimization component 2106 can include avariety financial models including option pricing models that considermaking a relatively small near-term investment that provides the optionof making a more substantial investment for economic benefit sometime inthe future when more information is known or there is greater certaintyof achieving the target return on the investment. The profitoptimization component 2106 also includes a stochastic model of theoperating scenario and external economic factors such as interest rate,labor rates, cost of capital, including international economic factorsthat will influence business. Other factors such as variability indemand and machinery reliability such as probability of failure in agiven time period given a particular equipment loading rate andmaintenance activity. This can permit balancing risk-benefit conditionsto match the operating and investment strategy of the organization.

Turning now to FIG. 22 which further illustrates 2200 the various anddisparate aspects and components that can be used in conjunction withcapacity management component 2102, energy optimization component 2104,and/or profit optimization component 2106, and that are integral aspectsof enterprise resource planning (ERP) component 184. As illustratedenterprise resource planning component 184 can include advisorycomponent 2202 that can utilize a decision-support framework, such asprognostics engine 110 or optimization engine 2210 (described infra),interpolated data as a function of historical data as well as knowledgeof dynamics of a system or process (e.g., model of a system or process)to create optimization visualizations. Advisory component 2202 can tiein financial information, production schedules, and the like, toquantify an enterprise manufacturing intelligence (EMI) system.Moreover, advisory component 2202 can employ drag-and-dropcapability/flexibility to handle “what if” scenarios. In this manner,advisory component 2202 can be utilized by plant facility management tooptimize production processes, and through facilities provided byvisualization component 2212 (discussed infra) such information or inputfrom advisory component 2202 can be used to provide visualizations ofproduction processes. It should be noted, that advisory component 2202can dynamically create an information model, and/or concurrently createa corresponding visualization.

Modeling component 2204 can also be included in enterprise resourceplanning (ERP) component 184. Modeling component 2204 can be utilized tobuild models or sub-models of demand and/or supply, for example, andassociated sources or sinks of such demand and/or supply. Creation ofsuch demand and/or supply models or sub-models can include utilizationof cost and efficiency attributes, and the like, and can also includeintegrating demand models with supply models. Moreover, demand and/orsupply models or sub-models can also be based on historical customerorders, order size, order accuracy (e.g., to minimize productionoverruns), order changes, etc. The developed or created models orsub-models can be employed to set inventory targets that can in turndrive or leverage capacity to meet demand which in turn can driveinventory management, ordering of factors of production, working capitaloptimization, and the like. Additionally, modeling component 2204 canalso construct and utilize stochastic models that can assess theprobability of achieving a stated economic return and/or one or moreoptimal operating strategy that satisfy all or some of the inputconstraints employed to develop the model.

As illustrated, enterprise resource planning (ERP) component 184 canalso include facility management component 2206 which can be utilized toidentify areas in a production process where inefficiencies are extantand methodologies and/or actions that can be utilized to resolve suchinefficiencies. In order to facilitate its goals, facility managementcomponent 2206 can employ the predictive capabilities of prognosticsengine 110 and/or optimization engine 2210 to tune the productionprocess to lower costs and to increase profitability. The predictivevalues generated can optionally include associated probabilistic valuessuch as for example, the likelihood of achieving the value and theprobability of staying at the predicted value for a specified timeperiod.

Moreover, enterprise resource planning (ERP) component 184 can includehierarchical component 2208 that can use multi-variant modeling and datamining to create hierarchical structures of a model of the productionprocess. The hierarchical structures generated by hierarchical component2208 can include or associate an organizational layer on top of themulti-variant model. For instance, multiple lines in a productionfacility can benefit from advanced process control (APC) from a model onone particular kiln or the like, and the model can be ported as a typeor class and can thereafter be ported to numerous and disparate lines ofproduction. It should be noted in this context, that Bayesian types ofmodels can be adapted based at least in part on specific use rather thanbuilding models from scratch each time, and that utilization of such aunified model allows for plant or production process design in a manneranalogous to the object oriented programming paradigm. Moreover, itshould be further noted that hierarchical component 2208 can also createbusiness system types of models. It should also be noted that the modelscan also include a suite of coupled sub-models that can be based onanalytic approximations of the production sub-processes. Alternativelyor in addition to the analytic models, production processes can bemodeled as causal models and key performance values extracted from thecausal or hybrid production models. The production processes can also bedescribed by other model-free estimators such as artificial neuralnetworks or a combination of model-based and model-free estimators.

In the context of modeling component 2204, facility management component2206, and/or hierarchical component 2208, the value of predictiveenterprise manufacturing intelligence (EMI) is typically a function ofthe model abstraction, and/or the plug-and-play nature of the models.Accordingly, utilization of the claimed subject matter can provide verysophisticated and unique “what if” situations that can be used to“sandbox” or prototype various production scenarios in order to maximizeprofits and minimize waste. A wide range of “what if” scenarios can begenerated and evaluated according to a cost function or economicvaluation method. Other generative and search methods such as geneticalgorithms may be used to search the space of feasible scenarios toidentify an optimal production scenario.

In accordance with an aspect of the claimed subject matter modelingcomponent 2204, facility management component 2206, and/or hierarchicalcomponent 2208 can develop and employ principal component type models(e.g., models running without any inputs—the model runs and evolves overtime). Such principle component type models can provide estimations ofattributes that typically cannot be measured with ease and further canprovide an understanding of how situations can evolve. Scenariogeneration and evolution can be described using a state transitionmodel. Values can be assigned to each state corresponding to theexpected return from operating in that particular production condition.State transition links can indicate the cost, risk, and probability oftransitioning to a neighboring more desirable or less desirable state.

Further, in accordance with further aspects of the claimed subjectmatter modeling component 2204, facility management component 2206,and/or hierarchical component 2208 can in conjunction or separatelyutilize global type models. Global type models can be perceived as atype of dynamic modeling for use with the unified production modelwherein various attributes of the model can be adjusted dynamically orin real-time. Moreover, in accordance with a further aspect of theclaimed subject matter, modeling component 2204, facility managementcomponent 2206, and/or hierarchical component 2208 can utilize existingor dynamically created models to dynamically and/or automatically (e.g.,recursively and/or iteratively) generate sub-models based at least inpart on physical changes to a production process and/or productionfacility.

The claimed matter therefore can provide a scalable platform thatprovides for advanced process control, optimization, and/or closed-loopcontrol systems. The matter as claimed therefore can verify and validateexisting or dynamically created models that can be implemented onlineand which can permit a local facility control engineer to interact withthe models. Additionally, by incorporating advanced process control(APC) aspects and utilizing financial information with the dynamicallycreated models, the models so generated can allow for cross-platformsharing of sub-models so that various vertical domains can share models(e.g., through utilization of cut and paste modalities) without thenecessity of domain expertise in the various areas of production or withthe associated models. Furthermore, the disclosed subject matter canbuild in constraints that prevent invalid models from being built orcreated. By building rich intelligence into developed or created models,when these models are deployed they can automatically, dynamically, andcontinuously learn the production process being modeled and in so doingidentify interdependencies or correlations to use in connection withfuture constraints that might arise in a production process. Inaddition, the claimed and disclosed matter can facilitate or actuate aninventory management aspect wherein production schedules can be employedto determine when and/or whether to order new inventory, or inventory ofbetter or lesser quality. For example, if a production process utilizesa factor of production with ash content, it might be determined throughutilization of the claimed matter that the ash content of the input issub-optimal in which case input with a higher or lower ash content mightneed to be ordered so that the production process can be renderedoptimal. The dynamically created models may run in parallel with theactual production process. Deviations observed between the model and theactual production process can form variances or residuals. The residualscan be analyzed and used to identify problems or faults in the equipmentor the process and permit efficient problem detection and diagnosis. Theanalysis of residuals can also indicate faulty assumptions or gaps inthe model. If faults are detected, the dynamic model can be used todefine and validate an alternative compensating production process thatwill mitigate the effect of the failed component or process untilcorrective action can be taken. Given that suitable reliability andproduction levels are met, the new production process can then beimplemented as an interim solution. Yet another role for the dynamicprocess model is to provide a basis for defining a new productionfacility or production process, The model can be used to define a new,superior model that provide improved economic return, less variability,and more robust production operation. Various potential productionprocesses can be generated and evaluated without the constraints imposeddue to existing, perhaps outdated, equipment, procedures, materials, andprocesses.

In a further aspect, enterprise resource planning (ERP) component 184can include optimization engine 2210 that can be applied beyondprocesses or control of processes to scheduling and/or economicoptimization of processes or production facilities management whereinsuch scheduling and/or economic optimization can be carried out inreal-time. For instance, plant or process scheduling can be carried outin real-time and can be based on current data. As will be appreciatedthe developed model (e.g., provided by modeling component 2204) can betightly coupled to live data and as such can be utilized to predictforward as part of the optimization process, marrying closed loopcontrol to key performance indicators.

In facilitating its aims, optimization engine 2210, as well as any othercomponent or aspect associated with enterprise resource planning (ERP)component 184, can utilize genetic algorithms as part of theoptimization process or in building models of production processeswherein inputs and/or outputs can be selected as part of building aprocess type. Further, optimization engine 2210 can determine (e.g.,learn) through data analysis what is to be considered as a normal modeof operation. In establishing a norm, optimization component 2210 canutilize a recorded expected behavior and compare it with actual behaviorto ascertain what should be considered normal. In such a manneroptimization engine 2210 can dynamically and adaptively adjustperformance indicators (e.g., key performance indicators (KPIs)) toreflect the reality of a particular production process rather than vaguetheoretical goals. Additionally, optimization engine 2210 also has theability to re-use key performance indicators (KPIs) and to obtaininformation from persisted sources (e.g., persisted or associated withstore 2216) as well as acquire data from known data sources which can beleveraged in connection with leveraging non-linear prediction models.The models generated can also have a stochastic measure assigned thatcan indicate the likelihood or certainty of the model and theprobability of achieving the expected production level or economicvalue.

Moreover, optimization engine 2210 can also be utilized to optimize theloading and unloading of resources. For example, optimization engine2210 in concert with radio frequency identification (RFID) tags can beutilized to determine how best to load or unload a container withproduct or raw materials. Similarly, optimization engine 2210 canfurther be utilized to best utilize empty space (e.g., shop floor space,office space, placement of raw material bins, hazardous materialhandling, . . . ). These facilities of optimization engine 2210 can beeffectuated through use of linear-regression modalities and/ortechniques (e.g., traveling salesmen type algorithms).

Further as illustrated in FIG. 22, enterprise resource planning (ERP)component 184 can include visualization component 2212 that providesvisualizations of its results (e.g., by way of automatically and/ordynamically in real-time updateable virtual instrumentation projectionthat allows user interaction). Visualization component 2212 can presentinformation in a new way, providing users the ability to look into theprognosticative future and/or to proactively adjust context. Forexample, a production facility engineer can reconfigure a productionfacility (e.g., plant or factory floor) to ensure that end-productoutput is maximized from every aspect of production. By employing theclaimed matter, and in particular, aspects of visualization component2212, multiple dimensions involved in the production of a final productcan be analyzed and negative factors mitigated and positive factorsenhanced in order to ensure maximum efficiency and maximum profitabilitythereby minimizing inefficiencies and loss. For instance, where an alarmshould have occurred but never occurred, visualization component 2212,through the facilities of other components and aspects included inenterprise resource planning (ERP) component 184, can provide anadaptive visualization of where the failure occurred. It should be notedin this context that the claimed matter automatically infers an event(e.g., alarm conditions, etc.) based at least in part on real-time inputor incoming historical data rather than on human input. Moreover,visualization component 2212 can facilitate or effectuate alarmclassifications thereby minimizing the occurrence of cascading alarmsand in so doing facilitating a root cause analysis to identify the rootcause of the alarm condition. For example, in order to identify the rootcause of cascading alarms the modeling structure can be beneficial as a“hierarchical alarm tree” can be developed as a consequence ofutilization of modeling component 2204 and can be utilized to prune the“hierarchical alarm tree” to ascertain the root cause of the cascadingalarms. The modeling structure can include a causal modeling componentand a stochastic modeling component and a state transition component.

Visualization component 2212 further allows production facilityengineers or production facility managers the ability, through useradaptable dynamic real-time visualizations, to predicatively identifyand/or isolate and resolve problem areas before these problem areasmanifest themselves in an actual production run. For instance,visualization component 2212 can be utilized to predictively visualizeand resolve a production event (or non-event) that will occur in thefuture (e.g., 2, 12, 24, 36, 48, 128, . . . , hours into a productionrun). Accordingly, for example, real-time control data (e.g., from oneor more industrial controller) can be utilized to automatically populatea predictive information model that can be developed by the claimedmatter. The predictive information model so constructed can then beutilized to provide rich visualizations that allows for gleaninginformation regarding a process or production system across temporalboundaries as well as potential optimization goals. Moreover, theclaimed matter can mesh real-time data with hypothetical data in orderto provide dynamically adaptive, predictive models. The predicted stateor states can have associated with them the probability the futurecondition or production event will occur and the probability it willoccur at a particular time in the future. This can permit taking actionsuch as altering the control, production rate, or equipmentconfiguration to avoid a problematic state or undesirable productionevent. Visualization component 2212 can include a facility foridentifying unusual or “interesting” conditions or events andhighlighting these in the presentation to the operator. The criteria forclassifying a condition as unusual or “interesting” can be made based atleast in part on the expect value or the value of the model-predictedcondition. In addition, persistent data and real-time data can beroutinely screened using established data mining techniques. Unusualconditions or trends can be identified and presented using visualizationcomponent 2212. Data mining techniques such as statistical measures(e.g., principal component analysis), artificial neural networks (e.g.,unsupervised Kohonen maps), and search agents (e.g., autonomous agents)can be employed to continually inspect the growing based of productionand economic data.

Additionally, enterprise resource planning (ERP) component 184 caninclude training component 2214 that can utilize previously constructedmodels to dynamically simulate various outcomes in order to provide atraining sandbox wherein apprentice users and/or seasoned professionalproduction facility managers can test various plant and productionconfigurations in order to learn the best ways of optimizing and/ormaximizing a production process. Alternatively, training component 2214can be used to inject serious fault and anomalous conditions todetermine the response of the system, the operator response, and thereaction of the system to the operator's response. A sequence ofstimulus-response events can be generated and evaluated. Trainingcomponent 2214 can include an evaluation module that can establish theskill level of the person being trained and identifies areas of strengthand weakness. Subsequent training and automatically generated scenarioscan be directed at improving the weak areas identified. The trainingmodule can optionally include an expert operator module and an expertteacher module. The expert operator module represents the response anexpert operator would have for different operating conditions. Theexpert teacher module assesses the students competencies and providescues as needed, permits exploratory search and investigation by thestudent, and at the appropriate time, give the student the correctanswer along with an explanation. During training, the trainee'sresponse may be compared to the expert operator modules and the expertteacher module will establish a student model that will guide theteacher module in determining the students competency and establishing ateaching strategy (e.g., immediately correct the student, permit thestudent to explore the implications of an incorrect decision, providehints or cues to the student, . . . ) and in carrying out the strategyand evaluating the students progression in learning. The training modulecan also include integrating real-time data to permit the student to seethe result of various decisions on an actual production process.

Store 2216 can also be included with enterprise resource planningcomponent 184. Store 2216 provides the ability to persist trajectoriesinto a historian aspect of the claimed subject matter. The historianaspect of the disclosed and claimed subject matter permits users (e.g.,plant facility managers, plant maintenance engineers, etc.) to informthe predictive and optimization aspects of the claimed matter (e.g.,optimizer engine 2210 and/or prognostics engine 110) with putativeconditions that the user deems necessary to a more efficient and/orstreamlined operation, the optimization and/or predictive aspects canthereafter provide models with which the user can interact andinterrogate and visualize (e.g., through visualization component 2212)the production process. As depicted store 2216 can include volatilememory or non-volatile memory, or can include both volatile andnon-volatile memory. By way of illustration, and not limitation,non-volatile memory can include read-only memory (ROM), programmableread only memory (PROM), electrically programmable read only memory(EPROM), electrically erasable programmable read only memory (EEPROM),or flash memory. Volatile memory can include random access memory (RAM),which can act as external cache memory. By way of illustration ratherthan limitation, RAM is available in many forms such as static RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink® DRAM (SLDRAM),Rambus® direct RAM (RDRAM), direct Rambus® dynamic RAM (DRDRAM) andRambus® dynamic RAM (RDRAM). Store 2216 of the subject systems andmethods is intended to comprise, without being limited to, these and anyother suitable types of memory. In addition, it is to be appreciatedthat store 2216 can be a server, a database, a hard drive, and the like.Store 2216 can include data in compressed or encoded form and can existin multiple distributed data stores. Data stores can reside in acomputer room, server room, computer-based production machine,programmable logic controller (e.g. PLC), intelligent device, or a smartsensor node and any combination of the above. Data can be accessedvirtually as if it was a central database residing in one location.

Additionally, enterprise resource planning component 184 can furtherinclude scenario generator 2218 that can automatically and/ordynamically generate and search through a wide range of plausiblescenarios and can select one or more optimal operating strategies thatcan satisfy some or all the input constraints. In facilitating its aimsscenario generator 2218 can utilize stochastic models that can assessthe probability of achieving stated goals, as well as can considertemporal aspects of plausible scenarios. For example, a high-returnscenario that lasts for a very short duration can be inferior to alonger term, more stable scenario that generates a slightly lesseconomic return.

FIG. 23 provides depiction of an illustrative method 2300 that can beutilized to provide an energy optimization model in accordance with anaspect of the claimed subject matter. Method 2300 can commence at 2303where variable costs associated with an entity's or organization'sbusiness system can be utilized to construct economic sub-models foreach energy-generating asset at a production facility. The sub-models sobuilt can be employed to determine each asset's financial profile,taking into consideration their respective generating capacity,efficiency curves, and operating costs. Other factors such asreliability, maintenance cost, and life-cycle costs can also beincluded. Each of these asset sub-models are then combined to create theproduction facility's energy-supply model. At 2304 the optimizationcomponent and/or prognostics engine of the claimed subject matter can beutilized to create a sub-model of production to determine, at auser-defined time horizon, the predicted energy demand based at least inpart on current and/or future operating objective. This sub-model can beconsidered the production facility's energy-demand model. At 2306 theenergy demand and supply models can be integrated utilizing the modelingframework of the claimed subject matter to solve for the economic supplyoptimum and expose the most cost-effective energy-generating assetavailable to meet predicted demand. This integrated demand and supplymodel becomes the energy optimization model. The energy demand andsupply models can be integrated in series, parallel, nested, or in anetworked structure to achieve the most efficient solution for aneconomic problem. The goal of method 2300 is to provide timelyvisibility into the most cost-effective source of energy to meet thepredicted demand from production, while ensuring full environmentalcompliance. Other factors such as probability the predicted energydemand profile will exist and the expected variability in this demand,supply equipment reliability, and certainty of providing the targetenergy levels in the future, the estimated cost in the future to providethe target energy level, the predicted cost of energy, and the estimatedcost of energy produced can also be included in the model.

FIG. 24 exemplifies an illustrative method 2400 that can be utilized toprovide dynamic capacity management in accordance with an aspect of theclaimed subject matter. Method 2400 can commence at 2402 whereascertainment can be made as to the current production rate. At 2404 aprediction (e.g., utilizing the various components associated withenterprise resource planning (ERP) component) can be made. Theprediction is the theoretical capacity of a facility's production. At2406 a visualization can be generated or more specifically projected orrendered onto a display (e.g., computer monitor, and the like). Thisvisualization can then be employed to drive towards the determinedtheoretical capacity as well as to identify bottlenecks to achieving thetheoretic goal. Moreover, the visualization can also be utilized toidentify to management historical bottlenecks and facilitate themitigation of such bottlenecks. As will be appreciated by those ofreasonable skill in the art, the visualization can also provideexecutives or production facility engineers the ability to redesign asystem or process in order to optimize the process as well as to makesmart financial decisions. The prediction of theoretical capacity in2404 can also include a cost function that assigns a cost to produce forthe various possible production levels. The cost function can includeenergy, support services, maintenance and reliability costs and otherlife cycle cost factors. This cost reflects the potential loss ofefficiency and increased failure rate when running equipment at or nearthe theoretical limit. It may indicate that it is not economicallyprudent to run equipment at the theoretical maximum capacity. Aneconomic optimization model can be used to establish an economicallyviable maximum capacity that may be less the physical theoreticalcapacity.

FIGS. 25-31 provide depiction of various illustrative visualinstrumentations that can be generated and displayed or rendered on adisplay device, for example. As will be appreciated by those of ordinaryskill, one or all the various and disparate visual instrumentations canbe simultaneously generated and/or displayed or rendered on a particulardisplay device. Moreover, it should also be noted that the generatedand/or displayed or rendered illustrative visual instrumentation can besubject to direct user interaction (e.g., using tactile manipulation).The displays can include a combination of persistent data, real-timedata, computed data, model-generated data, and user-entered data. Userinput permits exploratory searches and user-driven data analysis andscenario planning. As illustrated FIG. 25 provides a visualinstrumentation 2500 that depicts grade profitability over a timehorizon (e.g., the x-axis) measured in

uros/ton. Further FIG. 26 provides a further visual instrumentation 2600that depicts potential opportunity over a time horizon measured in

uros/ton. FIG. 27 provides visual instrumentation 2700 of the actualproduction costs of various factors of production (e.g., fiber,chemicals, steam, refining, blade, filler, . . . ) measured over a timehorizon. FIG. 28 provides depiction of a visual instrumentation 2800that illustrates various factors of production, the sell price and acomparison between the current grade and a theoretical target. FIG. 29exemplifies a further visual instrumentation 2900 that illustrates atheoretical vs. actual ash content (a factor of production). Visualinstrumentation 2900 provides comparison between the actual content ofash vs. the potential content of ash over a time horizon. FIG. 30provides another visual instrumentation 3000 that maps lost opportunitycosts over time and measure in

ros. FIG. 31 illustrates a further visual implementation that depictscontroller uptime and ash content in the current grade. FIG. 31 providesthe actual or current quantity, a target goal and categorizations ofpoor, fair, and good.

Although the invention has been shown and described with respect tocertain illustrated aspects, it will be appreciated that equivalentalterations and modifications will occur to others skilled in the artupon the reading and understanding of this specification and the annexeddrawings. In particular regard to the various functions performed by theabove described components (assemblies, devices, circuits, systems,etc.), the terms (including a reference to a “means”) used to describesuch components are intended to correspond, unless otherwise indicated,to any component which performs the specified function of the describedcomponent (e.g., functionally equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the invention. In thisregard, it will also be recognized that the invention includes a systemas well as a computer-readable medium having computer-executableinstructions for performing the acts or events of the various methods ofthe invention.

In addition, while a particular feature of the invention may have beendisclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application. As used in this application, the term“component” is intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited to, aprocess running on a processor, a processor, an object, an executable, athread of execution, a program, and a computer. Furthermore, to theextent that the terms “includes”, “including”, “has”, “having”, andvariants thereof are used in either the detailed description or theclaims, these terms are intended to be inclusive in a manner similar tothe term “comprising.”

What is claimed is:
 1. A device operable in an industrial automationenvironment, the device comprising: a processor configured to facilitateexecution of computer-executable instructions that perform operations,comprising: determining, by an artificial intelligence component, afirst probability representing a probability of achieving a predictedvalue associated with a defined capacity for a group of energygenerating assets included within a production facility; determining, bythe artificial intelligence component, a second probability representinga probability of sustaining the predicted value for a defined durationof time, wherein the probability of sustaining the predicted value forthe defined duration of time is determined based on a reduction ofemissions generated by the group of energy generating assets and aninterdependency between energy use data representative of energy usageby the production facility, energy cost data representative of costsexpended by the production facility, and life cycle data representativeof industrial machine life cycle costs associated with a group ofindustrial machines within the production facility; and as a function ofthe first probability and the second probability controlling the groupof industrial machines within the production facility.
 2. The device ofclaim 1, wherein the operations further comprise generating, by theartificial intelligence component, the predicted value based on aquality metric representing a product manufactured by the group ofindustrial machines.
 3. The device of claim 1, wherein the operationsfurther comprise generating, by the artificial intelligence component,the predicted value based on quality data representing feed stockquality information of feed stock used to produce a product manufacturedby the group of industrial machines.
 4. The device of claim 1, whereinthe operations further comprise identifying, by the artificialintelligence component, a critical state of a group of critical statesto avoid in regard to controlling the group of industrial machines. 5.The device of claim 4, wherein the critical state of the group ofcritical states is generated as a function of sensor data received fromthe group of industrial machines.
 6. The device of claim 5, wherein thesensor data represents temperature data received from a temperaturesensor associated with each industrial machine comprising the group ofindustrial machines.
 7. The device of claim 5, wherein the sensor datarepresents failure data representing a predicted bearing failure of abearing associated each industrial machine comprising the group ofindustrial machines.
 8. The device of claim 1, wherein the operationsfurther comprise identifying, by the artificial intelligence component,a desirable state of a group of desirable states to achieve in regard tocontrolling the group of industrial machines.
 9. A machine readablestorage device comprising executable instructions that, in response toexecution, cause a system comprising a processor to perform operations,comprising: employing an artificial intelligence component to determinea first probability representing a probability of achieving a predictedvalue associated with a defined capacity for a group of energygenerating assets included within a production facility; employing theartificial intelligence component to determine a second probabilityrepresenting a probability of sustaining the predicted value for adefined duration of time, wherein the probability of sustaining thepredicted value for the defined duration of time is determined based ona reduction of emissions generated by the group of energy generatingassets and an interdependency between energy use data representative ofenergy usage by the production facility, energy cost data representativeof costs expended by the production facility, and life cycle datarepresentative of industrial machine life cycle costs associated with agroup of industrial machines within the production facility; and basedon the first probability and the second probability controlling thegroup of industrial machines within the production facility.
 10. Themachine readable storage device of claim 9, wherein the operationsfurther comprise generating the predicted value based on a datarepresenting a product manufactured by the group of industrial machines.11. The machine readable storage device of claim 9, wherein theoperations further comprise generating the predicted value based on datarepresenting feed stock quality information associated with a group offeed stock utilized to produce a product manufactured by the group ofindustrial machines.
 12. The machine readable storage device of claim 9,wherein the operations further comprise identifying, based on use of animplicitly trained classifier, a critical state of a group of criticalstates to avoid in regard to controlling the group of industrialmachines.
 13. The machine readable storage device of claim 9, whereinthe operations further comprise identifying, based on use of anexplicitly trained classifier, a desirable state of a group of desirablestates to achieve in regard to controlling the group of industrialmachines.
 14. A closed loop monitoring and control system, comprising:an artificial intelligence component that determines a first probabilityrepresenting a probability of achieving a predicted value associatedwith a defined capacity for a group of energy generating assets includedwithin a production facility; the artificial intelligence componentfurther determines a second probability representing a probability ofsustaining the predicted value for a defined duration of time, whereinthe probability of sustaining the predicted value for the definedduration of time is determined based on a reduction of emissionsgenerated by the group of energy generating assets and aninterdependency between energy use data representative of energy usageby the production facility, energy cost data representative of costsexpended by the production facility, and life cycle data representativeof industrial machine life cycle costs associated with a group ofindustrial machines within the production facility; and a controlcomponent that based on the first probability and the second probabilitycontrols the group of industrial machines within the productionfacility.
 15. The closed loop monitoring and control system of claim 14,wherein the artificial intelligence component determines the predictedvalue based on a quality metric data representing a quality of a productmanufactured by the group of industrial machines.
 16. The closed loopmonitoring and control system of claim 14, wherein the artificialintelligence component determines the predicted value based on qualitydata representing feed stock quality information of feed stock used toproduce a product manufactured by the group of industrial machines. 17.The closed loop monitoring and control system of claim 14, wherein theartificial intelligence component identifies a critical state of a groupof critical states to avoid in regard to controlling the group ofindustrial machines.
 18. The closed loop monitoring and control systemof claim 17, wherein the critical state of the group of critical statesis determined as a function of sensor data received from the group ofindustrial machines.
 19. The closed loop monitoring and control systemof claim 18, wherein the sensor data represents temperature datareceived from a temperature sensor associated with each industrialmachine comprising the group of industrial machines.
 20. The closed loopmonitoring and control system of claim 14, wherein the artificialintelligence component identifies a desirable state of a group ofdesirable states to achieve in regard to controlling the group ofindustrial machines.